Geo-Spatial Knowledge and Intelligence

In order to diagnose the environmental problems and to protect the ecological environment timely, this paper take Leishan County as the study case and take the grid as the evaluation unit to construct evaluation index system of ecosystem sensitivity. Analytic Hierarchy process (AHP), regional synthesis method and the grid GIS technology are used in the evaluation system to the gridding expression of 10 m × 10 m grid scales. Spatial overlay method of raster data and evaluation model of ecosystem sensitivity comprehensive evaluation of the ecological sensitivity of Leishan County. The result shows: Leishan has a higher sensitivity, among which the non-sensitive area and slight-sensitive area are smaller, accounted for only 5% and 13.4% of the total area respectively; Followed are the middle sensitive area and extremely sensitive area, accounted for 26.3% and 17% of the total area; highly sensitive area is the largest, accounted for 38.3% of the total area.

According to the actual demand of land regulation, this paper presents a field patrol inspection system combining GPS and tablet PC through the integration of hardware and software. This paper has overall designed the function and framework of the field patrol inspection system. On the fundamental of solving some key technical problems such as serial communication between PDA and tablet PC and intelligent processing of date, this paper has developed a integrated field patrol inspection system by combining GPS and tablet PC on the basic of GIS. This technology is already verified in practice and proved to be feasible, which will improve the efficiency of land management and can achieve indoor and field integration of field patrol inspection work.

This paper is aimed to solve this kind of problem and cope with the actual requirements containing time window and dynamic demands. Therefore the smooth and continuous time dependent function is introduced and the two-stage model including the “initial optimization stage” and “real-time optimization stage” is established. At the same time, a hybrid algorithm based on genetic-tabu algorithm and simulated annealing algorithm is designed to solve the model. In the end the effectiveness of the hybrid algorithm and the model is verified by comparing the results of simulation and other algorithms.

The need for 3D visualization and navigation within 3D-GIS environment is increasingly growing and spreading to various fields. When we consider current navigation systems, most of them are still in 2D environment that is insufficient to realize 3D objects and obtain satisfactory solutions for 3D environment. For realizing such a 3D navigation system we need to solve complex 3D network analysis. The objective of this paper is to investigate and implement 3D visualization and navigation techniques and develop 3D routing instruction engine for indoor spaces within 3D-GIS. As an initial step and as for implementation a Graphical User Interface provides 3D visualization based on CityGML data, stores spatial data in a Geo-Database and then performs complex network analysis. By using developed engine, the GUI also provides a routing simulation on a calculated shortest path with voice commands and visualized instructions.

Aiming at the problem that saliency detection algorithms for low dynamic range (LDR) images are unsuitable for high dynamic range (HDR) images, we propose a new saliency detection method for HDR images, where the global and local cues are considered. Firstly, according to human visual perception of high dynamic range content, the luminance and chrominance are processed respectively. Secondly, the bottom-up saliency map (BU-SM) is obtained by the global information. Then, we construct the foreground and the background codebooks based on the BU-SM, and use the sparse coding to get the top-down saliency map (TD-SM). Finally, in order to well account for the global and local factors, BU-SM and TD-SM are combined to get the final saliency map of HDR images. The experimental results show that the proposed method is superior to the state-of-the-art methods.

To monitor the environmental parameters in vegetable greenhouse in real time and reduce the impact of climate disasters on vegetable growth, we develop a technology platform including the environmental data acquisition, transmission, disaster warning, remote control, and information push through using Internet of Things technology. The platform can achieve the greenhouse equipment control through using ZigBee, transmit the data to the cloud service center through GPRS, and be implemented through Java EE. The deployment of field tests in Beijing XiaoTangShan show that the platform is stable and reliable. Moreover, the platform satisfy the need of real time monitoring and early warning, and increase the management level in agricultural park facilities and the ability of coping with disasters.

Finding overlapping communities from social networks is an important research topic. Previous research mainly focus on static networks, while in real world the dynamic networks are in the majority. Therefore lots of researchers turn to study dynamic social networks. One specific area of increased interest in dynamic social networks is that of identifying the critical events. However these proposed algorithms more or less exist some problems. Here in this paper we propose a novel event-based framework for analyzing overlapping community evolution in dynamic social networks. In addition, we give an index that is community tag to depict the changing process of communities over time intuitively. Moreover five indexes based on events are presented to construct the neural network prediction model, only five indexes make the complexity computation of our prediction model is simpler than the existing algorithms. Experimental results show our framework performs better, and the prediction accuracy is also acceptable.

For using the formal method to deduce human creative thinking, Extenics deals with the contradictory problem by computer. Modeling novel mobile software to simulate flexibility innovation intelligence, and help people to develop creative ideas, to inspire innovation will become key issues to promote crowd innovation progress. According to Extenics innovation methods, new personal innovation mobile software is developed. A case shows that the Extenics innovation mobile software can help people to carry out effectively innovation activities.

Uneven clustering is one of the feasible methods for energy hole avoidance in a wireless sensor network (WSN). Usually all weight in clustering and routing, such as residual energy of a sensor, distance between different nodes, and so on, are invariable all the time. In fact, with the running of the network, the relative importance of all factors makes the change. A variable weight based clustering approach (VWCA) is composed in this paper for energy hole avoidance in WSNs. The characteristics of the paper are to adjust the weight of residual energy of a sensor and the distance between different nodes. Simulation results show that VWCA does better in energy hole avoidance among all sensor nodes and achieves an obvious improvement on the network lifetime.

In this paper we propose a new routing protocol-MDPRP for mobile wireless sensor networks, which adopts Markov Decision Process to make the decision of best next hop to forward the messages. In this scheme, we mainly integrate trust, congestion and distance as the main judgment criterion of the next hop decision. We evaluate the protocol by simulations and the performance results are encouraging.

The sequential pattern data mining technology is widely applied to various fields, and it brings an indispensable value for many areas, especially in the field of medical treatment. But the amount of health-care data is large and the information included is extensive, so some valuable information may have not been found, which needs us to take the further research. By using the Sequential Pattern Mining (SPAM) algorithm to deal with the health-care data, we try to find the user’s medical behavior and the doctor diagnosis model or rule. This article first introduces the characteristics and the worth of Medicare data and data mining on it, especially the sequence pattern mining significance. Then discusses the ideas and characteristics of SPAM algorithm and the advantages of high efficiency, we use SPAM algorithm to deal with health care data, and try to find the regularity of visiting doctor, medical treatment characteristics and drug-use mode about insured person in a certain period of time. The experiments reveal the treatment mode and characteristics of the drug of pregnancy, which can provide guidance and reference for the diagnosis and treatment.

The Ad hoc On-demand Distance Vector (AODV) routing protocol is a very important distance vector routing protocol in Mobile Ad hoc Networks (MANET). Due to the mobility of MANET, the performance of routing protocols in many scenarios is not ideal. Based on the consideration of the performance of intermediate nodes, this paper uses genetic algorithm to optimize the routing to find a more suitable route to improve the network performance. The simulation results show that GA-AODV has a significant improvement over AODV in average delay, packet received rate, and routing recovery frequency.

LXC is an OS-level virtualization technology supported by the Linux kernel. It can provide a lightweight virtualization technology support for PaaS cloud platform, in order to reach the goals that not only the different tenants are isolated but also the software as well as hardware system resources are shared. On the basis of analyzing the requirements of PaaS, a cloud resources management model of PaaS had been created by this paper based on the Cgroups mechanism of LXC. It conducted performance tests in terms of memory, CPU, disk and network transfer speed, etc., which was respectively to deploy various applications in the LXC-based and KVM-based PaaS. And then these performance results were analyzed and compared. The experimental results show that compared with KVM, the performance advantage of LXC is obvious, and it is very fit to be deployed in PaaS cloud platform of providing the high performance computing to ensure the computing high performance and high availability of PaaS cloud platform.

In complex networks, link prediction involves detecting both unknown links and links that may appear in the future. Recently, various approaches have been proposed to detect potential or future links in temporal social networks. To evaluate the performance of link prediction methods, precision are usually used to measure the accuracy of the predicting results. This paper proposes an algorithm based on the precision optimization. In the method, precision is treated as the objective function, and link prediction is transformed as an optimization problem. A group of topological features are defined for each ordered pair of nodes. Using those features as the attributes of the node pairs, link prediction can be treated as a binary classification where class label of each node pair is determined by whether there exists a directed link between the node pair. Then the binary classification problem can be solved by precision optimization. Empirical results show that our algorithm can achieve higher quality results of prediction than other algorithms.

Face detection is a classical problem in the field of computer vision. It is widely used in recent years, face detection and face tracking has not only limited to the scope of application of face recognition: in video retrieval, video surveillance, facial expression analysis, gender, race, age discrimination, digital entertainment, and so on. This paper proposed algorithm based on AdaBoost algorithm AAM model of face feature points to identify the improvement in a certain range to solve the present stage AAM algorithm does not consider the grayscale in the exact face of initial position and face face detection problem.

Stock price manipulation has become a big concern in stock markets, especially in emerging markets like China. This paper aims to employ machine learning methods to detect the stock price manipulation in China to increase the market fairness and transparency. Based on the information given by China Securities Regulatory Commission, we took the difference of stocks between manipulated time and normal time based on their daily return, trading volume, stock price volatility and market value. We used them as explanatory variables. Then we employed single model, Support Vector Machine (SVM), and ensemble model, Random Forest (RF) for detection. Test performance of classification accuracy, sensitivity and specificity statistics for SVM were compared with the results of RF. As a result, we found that both of them have a meaningful accuracy while RF outperforms SVM. We also found that daily return and market value have a bigger effect on detection than other explanatory variables do.

This paper adopted a new particle filter method to reduce the dimension of particle sampling during hand tracking and describes the posterior probability distribution of state variable with few particles. The manuscript presents three core issues: firstly, we studied the characteristics of relevant kinetics during the hand tracking and the operator’s cognitive psychology features under the man-machine interaction condition, and established a cerebellum prediction model by analyzing the operator’s behavioral characteristics during hand tracking; secondly, we studied the tracking algorithms related to the cerebellum model built; thirdly, we made a comparison with traditional particle filter algorithm through simulation. As shown in experimental results, the proposed algorithm in this paper can significantly improve both the tracking speed and precision.

Due to the strong randomness and nonlinear characteristics of the transmission line galloping, the prediction of the intensity and the characteristics (amplitude, frequency, trip rate, etc.) of the galloping cannot reach a high precision. A BP neural network model is employed to map three main meteorological factors and galloping trip-out risk. Three main meteorological factors, temperature, humidity and wind speed were used as input parameters and the risk of galloping trip as the output parameter of the model. Typical galloping data from State Grid Corporation were used to verify the validity of the model. In order to counteract random factors, the operations were performed for 20 times with the same training and testing data. All of the network results had more than 90% accuracy and the average rate was 92.3%. The results show that it is feasible to use this model to predict the risk of transmission line galloping trip. The research results can provide support for the transmission line galloping prediction and early warning technology, so as to improve the level of intelligent operation and maintenance of power grid.

To achieve accurate sleep stage classification and improve its generalization ability, we presented a features fusion method to classify sleep stage using Electroencephalogram (EEG) and Electromyography (EMG). We regarded EEG and EMG samples from MIT-BIH Polysomnographic database as analysis objects. First of all, we used the Discrete Wavelet Transform (DWT) to filter noise of signals and extract energy ratio of α, β, θ and δ wave from EEG and the high frequency component from EMG, and used Sample Entropy (SampEn) algorithm to extract nonlinear characteristics of EEG. Then, we compared the accuracy difference of sleep stage classification method between using EEG and using EEG and EMG features fusion by inputting these features to Support Vector Machine (SVM) classifier to train and test. Finally, we used cross-validation method to train and test different samples to verify its generalization ability. The experiment of testing accuracy showed a satisfactory result with accuracy of 91.86%, and the average accuracy raised 4.94% compared to the sleep stage classification method using EEG. The cross-validation results indicated that this method has better generalization ability.

Most of the community detection algorithms underperform on overlapping community structures. To eliminate the ambiguity, we define a membership function which can compute a node’s subordinate level to communities. This paper proposed a heuristic community detection algorithm with membership function (MCDA) utilizing which as a measure for community detection. By computing one node’s membership to each community, we can find which community it belongs to. Considering the edge connectivity, information transmission efficiency and other factors, some experiments are taken to demonstrate that the proposed algorithm perform higher accuracy and lower time complexity.

In order to solve the problem of cloud computing task scheduling in the process of total execution time and the total cost of the conflict, this paper used the cross entropy method to solve the problem of task scheduling in cloud computing. The experimental results showed that using the cross entropy method makes the task of the total execution time is the shortest, lowest cost, customer satisfaction is greatly improved.

This paper propose an optimized local outlier factor algorithm based on hierarchical clustering over grid bad measurement information, which affect the running safety of power grids phenomenon seriously. The method adopt statistical theory to evaluate the equipment running data and state information. Meanwhile, use clustering algorithm to analyze these data, to achieve the purpose of data reduction. While the relative entropy for data confirm the weight and thus enhance the accuracy of the algorithm. Experimental results show that the algorithm can quickly identify the bad power grid data.

With the development of vocational education in China, millions of students graduate every year. Many graduates can’t find the compatible job, even out of work. At the same time, many enterprises complain that the ability of graduates can’t meet their requirements. They found that it is difficult to find suitable talents. In recent years, publishing recruitment information through the Internet has become a common action of many enterprises, especially in the recruitment website. Because enterprises have clearly put forward the qualification and responsibility of posts that the candidates should have in the recruitment information, we designed and implemented a solution to help vocational colleges improving the employment rate and the quality of teaching. It crawl recruitment information from recruitment website and extract qualification and responsibility of posts. On the one hand, the recruitment information can help students obtain employment; on the other hand, the qualification and responsibility of posts can help vocational colleges to closely track the enterprises’ demand for talents’ vocational skill, formulate and optimize the talents training scheme timely so as to ensure the quality of talents.

A novel method using unclear norm minimization and weighted sparse reconstruction technology to detect crowd abnormal event is proposed. Given over-complete normal frames of video, low rank method is used to form dictionary and the corresponding eigen value of dictionary is also utilized to reflect their weight. With the dictionary and corresponding weight, we can detect whether the test frame is abnormal or not by analyzing weighted sparse reconstruction cost. Finally, experiments on benchmark datasets demonstrate the improvements of performance are 3–4% points on average and validate the advantages of our algorithm compared to the state-of-the-art methods.

According to the quality requirements and business characteristics of power grid dispatching automation system software, the author proposed a software quality evaluation model that suitable for power dispatching automation system, meanwhile combining with quality methods of the current common software. The quality model was decomposed into quality characteristics, quality sub-features and metric elements. The author used analytic hierarchy process to establish the evaluation index system and determined the index weight, and used the fuzzy evaluation method for quality evaluation. The technology provides an important basis for the quality control of power grid dispatching automation system products, and ensures the power grid dispatching automation system operating in a safe, stable and reliable way.

A calculation program of projectile PRC (Plateau Resistance Coefficient) identification was wrote under Matlab platform based on CK (Chapman-Kirk) method of aerodynamic force parameter identification and shooting range test data of a certain type of shrapnel in this paper. And the difference of resistance coefficient between plateau and plain was analyzed, which showed clearly that, the former was 10–40% less than the latter. The reducing of the PRC would bring 4.24–5.93% farther more range to the field of fire. It was consistent with the plateau test results. It indicated that the change of the PRC and its effect must be considered and calculated particularly when deciding plateau shooting table of the certain type of shrapnel, studying of projectile plateau ballistic characteristics, or designing the plateau module of the artillery information system.

Image semantic segmentation is a popular research direction in the computer vision field. Semantic segmentation algorithms based on deep learning outperforms the traditional methods. Fully convolutional neural network (FCN) whose fully connected layers are transformed into convolution layers is a kind of convolutional neural network (CNN). In this paper, FCN is used to operate the image semantic segmentation, which could take input of arbitrary size image and implement end-to-end segmentation task. Due to the limited number of training images, some layers are fine-tuned from AlexNet and the dataset is enlarged by mirroring. The hierarchical feature maps from FCN are combined to improve the segmentation effect. Conditional random fields (CRF) is used on the segmentation result of FCN, which takes into account the positional relationship and color features between any two pixels. Experiments show that our method could refine the segmentation result of FCN, especially using CRF as post-processing.

Laser point cloud is currently a hot three-dimensional study. Equipment used in this article are self-developed by Capital Normal University. This thesis mainly focuses on scan the Zhongshan Park by mobile horizontal pushing type and obtain the distance and angle information by 360º lancer scanner to figure out the 3D laser point cloud relative coordinate, and make color bleeding with image information obtained by linear CCD camera. Then, we get color point cloud information. The scattered point cloud is integrated based on the reaction of echo date to different target objects, and the visual modeling towards historic building will be realized. The 3D laser image scan data and 3D modeling and historic building will play an important role in protecting of historic building.

For the importance of intersection in the traffic network, we introduce the concept of node degree into the traffic network fractal calculation. A weighted node degree-radius dimension is designed to better characterize the variation of network connectivity from center to periphery. The road networks of Chengdu city have been analyzed. The research shows that: the node degree-radius dimension can reflect the network characteristics, the weighted dimension will better reflect the spatial connectivity structure of urban system; the truck road is ringy and radial, it is more balanced than overall network, the contact between the second layer and the third layer in Chengdu city is not close enough, peripheral road network has a lot of space for development.

In recent years, indoor positioning technology based on WIFI has been widely researched. However, traditional WIFI-based indoor positioning method can’t achieve high localization accuracy due to the clustering errors at some locations. In this paper, RSS and location based twice clustering (RLTC) and Multi-user Topology Approximation Algorithm is proposed. The algorithm is divided into two stages. RLTC method is proposed during offline stage to correct clustering results. During online stage, multi-user topology approximation method is proposed to reduce positioning error on some particular location. Experiments show that the proposed algorithms can effectively improve the positioning accuracy compared to traditional positioning method.

Phenology plays an important role in understanding the feedbacks of plants to climate change. However, phenology observation is not a trivial task, particularly for large covers with huge diversities of plants. To handle this issue, this study attempted to apply wide-spread surveillance cameras (SCs) for plant phenology monitoring. In the case of flowering phenology, multiple phenological indices were proposed and derived from SC image series to identify the starting and ending dates. Test showed that the derived flowering phases for Robinia pseudoacacia (Fabaceae), Prunus cerasifera (Prunus) and Malus micromalus (Malus) agreed well with the ground-truth data. The feasibility of assuming SCs for plant flowering phenology monitoring was validated. Furthermore, this study alludes to a potential way of using SCs to compose regional- to continental-scale networks for phenology monitoring.

With the acceleration of urbanization, road congestion is getting worse. To quickly determine the traffic jam, taking the example of Jiashan traffic data collected by ground sense coil. The paper firstly cleans raw data, eliminates error data, supplements missing data and processes redundant data, which provides reliable data for visual analysis; then using different visual components select congested roads, and collaboratively interacts three visual component which including heat map, bar graph and chord diagram, visually expresses road congestion from macroscopic to microcosmic and judges the position of the traffic jam, and provides reliable route for public trip.

Public safety problems increase in recent years and bring a big challenge in the field of fast data acquisition for decision making. UAV (unmanned aerial vehicle) system as an effective way to take images of the emergency scene is highly demanded in many public safety application fields, and the three-dimensional (3D) reconstruction of the scene based on the captured images is also increasingly required. However, multi-lens camera system for taking titled images has not yet maturely applied to small UAVs. In this paper, a novel multi-lens camera system adapting for small UAVs is designed to capture the needed 3D reconstruction images. This system is also able to provide the possibility of transmitting the images to the ground station in real time for further consequent 3D reconstruction processing.

The LAI is the key factor which has an important influence on crop growth. LAI inversion from remote sensing is an important work in crop management. While, the accuracy of LAI inversion from remote sensing data is restricted by the limited number of observation. Multiple-sensor method has been proposed by the researchers. In this study, two sensor remote sensing data (HJ-1A/CCD1 and GF-1/WFV1) were collected in the study area. The random forest regression (RFR) was adopted in LAI inversion. The MODIS LAI product and the measured wheat LAI were used to calibrate and validate the LAI inversion model. The four spectral indices (DVI, SR, EVI, and SAVI) based on remote sensing data were calculated to develop the LAI inversion model. The accuracy of inversion of wheat LAI by remote sensing image can be improved by adding observations of angle data. Our data analysis resulted in an accuracy of R2 = 0.36, MAE = 0.467, and RMSE = 0.613 for the measured LAI. And in the validation by MODIS LAI product, an accuracy of R2 = 0.48, MAE = 1.05, and RMSE = 2.72 was found, which was a little greater than the average accuracy of mono-angle data for inversion of LAI. The result indicates that the reasonable combination of multi-sensor data can improve the accuracy of LAI estimation.

In the image de-noising based on wavelet transform, the selection of wavelet base remarkably influences the result. In the paper, a new quantitative method using analytic hierarchy process (AHP) to assess wavelet base was introduced. Seven indexes were selected to assess the result of image de-noising; a relative importance judgement-matrix was built and weights of each indexes were quantitatively analyzed based on AHP; the basic frame to assess wavelet bases was built. Living examples show that the method proposed is subjective, convenient and practical which can select the optimal wavelet base intuitively and assess the selection of wavelet bases effectively. The method conquers the shortcomings of traditional methods that are determined depending on experiences and lack of subjective quantitative assessment, which is of practical significance.

Squid (Ommastrephes bartramii) is a kind of economic fishery resources. Artificial light at night attracts and aggregates fish and squid because it mimics light produced by bioluminescent marine animals. The fish men use lamps to aggregate squid for squid jigging. So the squid fishing vessels can be detected by the sensor of Operational Linescan System (OLS) on Defense Meteorological Satellite Program (DMSP). This article mainly used OLS shimmer data from DMSP-F18 satellite covering the Pacific northwest ocean and estimated the quantity of squid jigging fishing vessels close to the terminator to expound the method that people can utilize the night-time satellite remote sensing data to estimate the quantity of light-trapping fishing vessels.

It’s critical to carry out comparison among remotely sensed data products and choose appropriate oceanic primary productivity product to apply to evaluation of fishery resources, ocean carbon cycle and global change research. This paper mainly gives an intercomparison of three ocean net primary productivity (NPP) products for the open South China Sea (SCS) estimated by Standard VGPM, Eppley-VGPM and CbPM models from MODIS. Data preprocessing and intercomparison work including image reading, cropping, invalid values removing and calculating average were solved by MATLAB. There are significant differences among the annual ocean NPP averages estimated by the three models within one year. The differences of seasonal variation among the three products are different, the values estimated by Standard-VGPM are highest in winter and lowest in spring. The ocean NPP values estimated by the three models are all highest in 23°N. The NPP values are high in shallow offshore area and then decreasing with the increasing distance away from the coast, and the maximum and the minimum values of the three models are different. According to previous researches, NPP products estimated by Standard-VGPM compared with other two models are more suitable as the application data in the open SCS. In general, it’s necessary to consider and improve the precision problem of the ocean NPP estimated by the algorithms based on inversion data from remote sensing.

The main water of Tarim Basin is from snowmelt and precipitation in the mountainous area by surface runoff into the basin. The extraction of hydrological information and analysis of hydrological characteristics was conducted based on DEM, through studying the basic principle of watershed extraction, and using ArcGIS hydrology toolbox in this paper. The results show that the river network in the Tarim River Basin extracted by ArcGIS is basically the same, compared with original nine rivers flowing into the mainstream.

Using ASTER image as data source, the data were preprocessed based on RS software, for different types of erosion and alteration minerals. Using the main principal component analysis (PCA) and band ratio together for alteration information extraction and comparing the extraction results with the known ore occurrences. The results show that: the method combining principal component analysis and band ratio together in extracting mineralized alteration has certain feasibility, the test of alteration information extraction in Qinghai Lalingzaohuo region confirms that the extract information with the known ore occurrences are in good agreement, which has certain reliability.

For rapid development and wide application of UAV, high-resolution imaging radar suitable for UAV is more and more requirement. Miniature Synthetic Aperture Radar (MiniSAR) can be equipped with medium/low altitude UAV platform, which applies to the remote sensing tasks in hazardous and harsh conditions. Thus, it plays a pivotal role in geographical situation monitoring. The merit of high-performance MiniSAR is light weight, low-cost, low power consumption. Moreover, it has high resolution, interferometric and fully polarimetric imaging capabilities. Currently this paper does introduce the hardware components, system functionality and property of MiniSAR. The system is employed for the flight testing in Jishan County of Shanxi to achieve fully polarization and interference SAR imagery with 0.3 m resolution. Considering the fact that the fully polarization SAR imagery contains abundance of ground target’s backscattering characteristics, it can be available for terrain classification, crop monitoring and other fields. For the SAR interferometry, DSM and DOM are obtained after phase unwrapping, block adjustment and other interference processing. Finally, the accuracy is verified using checkpoints. The result meet the requirements of topographic mapping at a scale of 1:5000. The applicability of MiniSAR system in the application of geographic condition monitoring is verified.

Visual quality assessment of stereo image plays an important role in three dimensional visual communication. Considering the processing of binocular perception in viewing stereo image, we present a reduced-reference (RR) stereo image quality assessment (SIQA) model based on binocular perceptual characteristics. Firstly, stereo images are divided into binocular fusion portion and binocular rivalry portion with internal generative mechanism. Then, cyclopean view is generated according to the binocular fusion portion and binocular rivalry portion with binocular perception, and the Gaussian scale mixture RR features from cyclopean view and the binocular rivalry portion for SIQA. Finally, the quality indicators of cyclopean view and binocular rivalry portion are computed to obtain the final SIQA score. The proposed model is tested on the LIVE 3D IQA database. Experimental results show that compared with the state-of-the-art methods, the proposed model has high correlation with subjective perception and can evaluate human stereo visual properties effectively.

High dynamic range image can provide wider dynamic range and more image details, it is needed a tone-mapping operator in order to be showed on an ordinary display, how to evaluate the tone-mapped image becomes an important problem to be solved. The distortion of tone-mapped image is different from the traditional image distortion, so, this paper proposes an objective quality evaluation algorithm of tone-mapped image based on color space which considers the difference between the reference and test images, the structural fidelity, the color distortion and the naturalness of the test image. Finally, the support vector machine is used as the pooling strategy to set up the quality assessment model. The experimental results show that the Pearson linear correlation coefficient of the proposed method is about 0.86, the Spearman rank correlation coefficient is about 0.84, which means that the proposed method is consistent with human visual perception.

In this paper, a modified nonlocally centralized sparse representation method is introduced, which is suitable for removing both the non-sparse noise and sparse noise such as salt and pepper noise, periodic noise, and mixed noise in particular. In the proposed method the conventional median filtering is embedded in nonlocally centralized sparse representation. The main advantage is that it can attain better performance for various common noise, and significantly superior for mixed noise. The effective and efficient of the proposed method is demonstrated experimentally.

Detecting and tracking aviator’s hand in the cockpit is a fundamental task on analyzing and identifying the behavior of aviators. Due to the complicated conditions in the cockpit - such as the lighting varies, the space of Cockpit is narrow, the operation of aviator is sophisticated - tracking the hand in Aircraft Cockpit has more difficulties than tracking the hand in human-machine interaction. We propose a hand tracking method to track the aviator’s hand based on depth images. In our experiment, most of the common flight operations are tested. The average error of hand position tracking is 6.4 mm and the ratio of losing tracking is only 1.4%, which indicate that the proposed algorithm has the ability to tracking the aviator’s hand in the aircraft cockpit accurately.

Reachability over a massive graph is a fundamental graph problem with numerous applications. However, the concept of a classic reachability algorithm is insufficient for a temporal graph, as it does not consider the temporal information, which is vital for determining the reachability between two nodes. In this paper, we propose an efficient algorithm for answering the reachability problem in a temporal graph. Our approach fully explores the time constraints among edges in a temporal graph, and utilizes an index to speed up the computation. Furthermore, online graph update is supported. Various experimental results demonstrate that our algorithm outperforms competitors greatly.

LIDAR integrates digital camera, global positioning system (GPS), inertial navigation system, laser ranging and other advanced technology,using GPS to control the attitude of flying platform, obtaining the distance information between receiver and landmark via laser, capturing the image information by digital camera. At last, the distance is expressed as the 3D coordinates of the target landmark, and the image information is expressed as the surface characteristics on the target landmark. In this paper, firstly, the 3D coordinates are speckle noise filtered to realize point cloud de-noising and classification; then the point cloud of classification is plain rasterized to achieve the transformation from plane coordinates to image coordinates, and the image coordinates is processed to be corresponding elevation of grid unit by incremental insertion and median filter, at the same, the elevation is filled to the corresponding grid to generate DSM depth image by Gray-Scale transformation. Finally, the SOBEL detection operator is quickly and efficiently used to extract the building boundary characteristics which are applying in digital city construction, three dimensional entity model construction, urban planning and construction.

Hyperspectral mineral identification methods have a wide application in field. In this paper, we propose a new pipeline of mineral identification by using absorption band spectrum features. In the pre-processing, a local spectral continuum removal algorithm is used to normalise the corresponding spectral data of the absorption band of different minerals. After that, the polynomial fitting is applied to remove the spectral outliers. Then, by using the preprocessing spectral data, the absorption band spectrum features are extracted to establish a rules based interring system for minerals recognition. Experimental results on hyperspectral images demonstrate that the proposed method has good performance in minerals recognition.

Surface albedo is one of the main factors in climate modeling, and is widely used in the energy balance analysis for the coupled atmosphere and surface system. While the surface albedo of the Tibetan Plateau, located in the southwest of China, is of great significance to the study of China and the global climate. This paper aims to analysis seasonal changes of surface albedo in Tibetan Plateau by using MODIS (MODrate-resolution Imaging Spectro-radiometer) global albedo product and land cover type dataset, and to obtain statistically the spatial and seasonal variations over this region.

The multiple watermarking algorithm is present for vector geographic data based on the characteristics of vector data. In the embedding progress multiple watermarks were embedded additively in the cover data following additivity rule. In the detection progress the additive watermarks were extracted and then the contents of watermarks were detected on the basis of correlation detection together with discriminant analysis. In the experiments the robustness and adaptability were analyzed. The results show that the proposed multiple watermarking algorithm is robust against common attacks and suit for the vector geographic data with small amount of vertexes.

The volume of spatial data increased tremendously, and growing attention has been paid to the research of distributed system for spatial data analysis. Spark, an in-memory distributed system which performs much better than Hadoop in speed and many other aspects, lacks spatial SQL query extensions. In this paper, we study the technology framework of Spark SQL, and implement the spatial query extension system tightly combined with the native Spark system. The extensions in the system include spatial types, spatial operators, spatial query optimizations and spatial data source formats. The spatial extension system on Spark retains the scalability and can be further extended with more query optimizations and data source formats. In this paper, the spatial data type system and spatial operator system follow OGC standards. In addition, the extension method is also a general method of query extensions on Spark SQL in other fields.

To monitor the entire earth system, several global land cover mapping products have been produced based on different remote sensing imagery (e.g., AVHRR, MODIS, SPOT, HJ-1), including UMD Land Cover, Global Land Cover 2000, GlobCover 2009 and GlobeLand30. However, the application potential of those products has not been fully explored at a provincial scale. The primary objective of this study is to compare and investigate the potential of these products in Anhui Province, China. The area and spatial consistency were used to evaluate the four datasets based on China’s GlobeLand 30 due to 30 m spatial resolution and the total accuracy of 83.51%. The Pearson’s correlation coefficient (R) and the percentage disagreement (PD) were used to evaluate the area consistency. The spatial similarity coefficient (O) was here adopted in order to verify the accuracy of spatial positions. A total of eight cover classes including cropland, woodland, grassland, shrubland, wetland, water bodies, artificial surfaces and bareland were reclassified and mapped to perform the intercomparison. The analysis results show that the PD of GlobCover 2009 is respectively 15.36% and −8.43% for “cropland” and “woodland”, while they are −20.20% and 2.10% for UMD. The “woodland” has better agreement percentage in comparison with “cropland”. The O of UMD is the worst of 69.25%, indicating that the spatial consistency is weak for “cropland”. Conversely, the O of GlobCover 2009 reaches up to 83.50%.

Carbon and water cycle of terrestrial ecosystem is the frontier scientific issues of global change and carbon cycle research, the process mechanism of carbon and water coupling analysis is the scientific basis of climate change mechanism, climate change prediction, and climate change adaptation strategy formulation. For the arid and semi arid region, which accounts for 30–50% of the total land area in the earth, the carbon source problem still has a lot of uncertainty, and the combination research of the two processes of carbon and water is very little. The analysis about research results from domestic and foreign related ecosystem carbon and water coupling relations not only can understand the carbon and water coupling effect in different scale and internal relations but also contribute to a profound understanding of the effect of temperature and precipitation patterns change on the water and carbon balance of ecological system in arid area under the context of global change, which will help to grasp the dynamic ecological system in arid area. This paper analyzes the coupling relationship between carbon and water, water use efficiency and carbon and water coupling model, and focus on carbon and water coupling relationship of ecological system in arid area. Meanwhile, this paper studies carbon and water fluxes in different spatial and temporal scales of desert riparian forest ecosystem, compares the water efficiency of desert riparian forest ecosystem system (WUE) in different scales, and explore carbon and water coupling relationship of desert riparian forest ecosystem and water use efficiency change under the background in order to study the coupling model based on process.

Selecting a typical karst rocky mountain area as a study case located in southeast of Yunnan province, getting Landsat5 TM digital images detected from two different time point of 1990 and 2007 year, karst rocky desertification condition is monitored and change analysis is completed by use of remote sensing process and GIS spatial analysis technique integrated land use diagnoses. The results are shown that: the dynamic changes of the karst rocky desertification amount were not distinct from 1990 to 2007, but the amount of the different degree types of karst rocky desertification changes relative obvious, in general karst rocky desertification condition presents a severity trend. The study also demonstrates that the integrated technique application by remote sensing, GIS and land use diagnoses can get the past back data and landscape pattern with location and quantity. This is help to understand deeply the development characteristic of karst rocky desertification change, and be benefit to provide decision-making support for karst rocky desertification mountain land use and management.

In order to understand and grasp the anisotropic characteristics of crustal media in 12 fixed seismic stations of Longtan Reservoir Digital Telemetry Seismic Network (LRDTSN) from impoundment in October 2006 to July 2013, on the basis of precise positioning of seismic events in Longtan Reservoir, using SAM comprehensive splitting analysis method, which contained correlation function calculation, time delay correction and polarization analysis and test, the effective dominant shear-wave polarization direction and delay time (shear-wave splitting parameters) in 9 fixed stations of LRDTSN were calculated. Analysis showed that under the influence and control of principal compressive stress and regional faults in the South China Block, fast shear-wave polarization directions in stations of LRDTSN had obvious local characteristics. The principal compressive stress directions within the scope of Longtan Reservoir included the dominant polarization directions of NW and NNE. Under the impact of loading and unloading water during impoundment of Longtan Reservoir, the spatial distribution of slow shear-wave normalized delay time was uneven, high in the northwest of dam area and slightly high in the periphery of dam area. Meanwhile, shear-wave splitting parameters in some stations had certain correspondence with the water level of reservoir.

Tsunami is a kind of wave with great destructive power, which has great impact on the environment. Numerical prediction is an important way to reduce the environmental disasters. The inversion-forecasting model is an important method for the prediction of tsunami. In this paper the Japan’s east coast was divided into 18 unit sources to set up a tsunami database by using COMCOT numerical model. An inversion forecasting model was established by using least non-negative square method based on the database. The model was applied to 2011 Tohoku tsunami, the initial tsunami water level with 10 m increase and 3 m decrease calculated by the model were basically the same as previous research, the buoy level of prediction is in good agreement with measured data. Comparing with tsunami heights measured by tidal stations at coastal area of Zhejiang Province, the deviation of forecasted and measured value is large. But the prediction accuracy can be greatly improved by solving COMCOT nonlinear equations with source parameters inversed by the inversion forecasting model. This study has significance for reference of East China Sea tsunami forecasting mechanism.

According to the results of the remote sensing satellite data of HJ, TM and MSS in the recently 40 years. The area of grassland desertification was increasing trend around Qinghai lake area from 1975 to 2000, whose rate was 12 km2/a, the area change of grassland desertification was small during 2000 to 2008, which was stable. The area greatly reduced in 2008–2012, reaching to 56.90 km2. This shows that the reduced area of grassland desertification is more obvious in the Qinghai Lake area in the past 10 years. Meanwhile the ecological environment tends to be improved. The main driving force of desertification area decreases is: The water level increased of the Qinghai lake is significant in nearly 10 years; The climate of Qinghai Lake basin shows warm and wet trend. It is particularly prominent after entering the 21st century; the runoff into of the increased lake is also obviously; Human activity is slowing around Lake area.

Leaf area index (LAI) is an important indicator of crop growth status. In this paper, the relationships between canopy reflectance at 400–2500 nm and leaf area index (LAI) in pepper crop were studied. 102 pair of canopy reflectance and LAI of pepper were collected in 2014–2015. Reflectance of canopy were measured in the field over a spectral range of 400–2500 nm. Simultaneously, the LAI were collected by the LAI-2000. Estimation models of LAI were developed based on the whole spectrum range by partial least squares regression (PLSR) and support vector regression (SVR), respectively. Then the field canopy spectra were resampled according to the band response functions of seven satellite sensors. They were the Vegetation and environment monitoring on a new micro-satellite (VENμS), Worldview-2 (WV-2), RapidEye-1 (RE-1), HJ1/CCD1, Sentinel-2, Landsat 8/OLI and GaoFen (GF) 1/WFV1. The values of common used spectral indices were calculated based on the simulated sensor bands, respectively. Prediction models were also developed based on the spectral indices and simulated bands. The results showed that the PLSR model by whole spectrum had the good accuracy of LAI estimation with the R2c = 0.726, RMSEc = 0.462, R2cv = 0.635, RMSEcv = 0.538. For the simulated satellite datasets, the better LAI estimation were obtained by Sentinel-2 and Venμs bands with the R2cv greater than 0.600 and RMSEcv less than 0.557. The Estimation model by simulated WV-2 bands, and RE-1 bands had the lowest performance with the R2cv between 0.50 and 0.55, and RMSEcv between 0.600 and 0.623. The inversion results demonstrated the potential of the multispectral remote sensing data to calibrate the LAI estimation model of winter pepper for the precision agriculture application.

Fog is a phenomenon that affects the Indo-Gangetic Plains every year during winter season (December–January). This fog is sometimes in the form of radiation fog and other also occurs as a mixture with other gases, known as smog (smoke + fog). There are various factors contributing to the formation of fog, that may be either meteorological, topographical or resulting from pollution. Fog has been mapped for the winter seasons of the years 2002–2016. In these winter seasons, fog affected areas were found to be changing significantly. The net cover of fog during a season varies in space, time intensity and frequency of occurrence. Presently, it is now possible to map and to predict fog formation to some extent. However, so far it has not been possible to disperse fog, though theoretically it has been discussed in literature. In the current work, experiments were conducted to find out the possibility and effectiveness of a negative air ionizer for fog dispersion. The experiments were carried out with fog, dhoop smoke and a mixture of both to generate smog. Two different glass chambers of different sizes were used in a closed room and the impact of air ionizer on dispersion was studied by testing the time taken for dispersion with or without the ionizer. The results show a significant performance with air ionizer indicating the effectiveness of the ion generator, which reduced the time taken for dispersion (in comparison to without ionizer) by about half.

Based on 14 ENVISAT ASAR data sets, the ground deformation information of Chengdu Plain during 2008 to 2010 was acquired using SBAS-InSAR method. The result showed that the average surface deformation was between −8 to 14 mm in major cities in Chengdu Plain during this monitoring period. The subsidence area in the north of Chengdu and south to Deyang City is maximum to −22 mm with an expanding subsidence area as time goes by. Chengdu Plain has no regional tectonic setting of subsidence and has abundant groundwater resources, thus there is no large-scale subsidence funnel. The monitoring results are validated by measured data and the accuracy is 2.9 mm. This outcome can provide a reference for bettering the future surface deformation monitoring for major cities in Chengdu Plain.

The Shillong region falling in north-eastern part of India is developing fast and has witnessed one of the greatest earthquakes of India in 1897 besides several other earthquakes. Seismic hazard assessment of the Shillong and adjoining regions has become pertinent and been carried out with the help of GIS in view of sharp increase in human activities and the proposed developmental works. For this purpose an area has been selected spanning from 24.2° to 26.8°N latitudes and 89.2° to 92.8°E longitudes and is divided into a 0.2° × 0.2° grid-like framework for analysis. Seismic hazard assessment has been performed by employing deterministic seismic hazard assessment (DSHA) approach. Further, 1897 earthquake rupture zone based ground motion estimations were also done in order to examine and compare the intensity converted ground motion and predicted ground motion in the epicentral and nearby areas. Maximum predicted ground motion is found out to be 0.606 g on the rock sites and 0.634 g on the soil sites. The average ground motion on the rock and the soil sites is 0.433 g and 0.26 g respectively.

Soil erosion is currently one of the main research topics of environment change, and it has affected the human survival and sustainable development. In this paper, Revised Universal Soil Loss Equation (RUSLE) based on RS/GIS technology was used to estimate soil erosion in years of 2001, 2007 and 2013 in the Ninghua County Fujian Province incorporating spatial analysis method based on GIS, temporal and spatial dynamic changes. For analyzing the temporal variation of soil erosion intensity in different periods in the study area, we investigated the variation tendency of soil erosion intensity each of the land use classes, topographic factors (elevation and slope) and vegetation coverage factors. The results indicated that RUSLE model based on RS/GIS technology could be extended to estimate regional soil erosion, and provided effective technological means for quantitative analysis of regional soil erosion.

In order to reveal the dolomite strata developed cave system in the cave water gas CO₂ partial pressure of cave water hydro chemical process and its dynamic characteristics and control factors. The dynamic monitoring for a period of year from the water chemistry index of Suiyang Shuanghe cave water from June 2015 to Mar 2016. Results show that: 1. Hydro chemical types of Shuanghedong cave water is mainly $$ \rm{HCO}_{3}^{ - } \hbox{-} \rm{Ca}^{2 + } \hbox{-} \rm{Mg}^{2 + } $$ and $$ \rm{SO}_{4}^{2 - } \hbox{-} \rm{HCO}_{3}^{ - } \hbox{-} \rm{Ca}^{2 + } \hbox{-} \rm{Mg}^{2 + } $$ type water and got overlying gypsum strata and $$ \rm{SO}_{4}^{2 - } $$ become dominant ions and can greatly improve the solution Ca2+ ion content. 2. Cave drip water - gas phase CO₂ partial pressure difference (ΔP_CO2) between SIc and has good negative correlation at the same time, two important mineral saturation index of SIc, between SId exist a good positive correlation. 3. The analysis of the measured data proves the objective existence of the “actual degassing and deposition” and the difference between the theory and practice of the classical degassing and deposition theory. 4. The liquid phase CO₂ (P_CO2(water)) in the cave water showed a significant negative correlation with the pH value of the solution, and the CO₂ partial pressure of the cave air in the undeveloped natural closed cavity had little influence on the water of the cave.

In recent years, China has been plagued by the problem of air pollution. Considering the great harm of air pollution to human and the global environment, Atmospheric environmental problems have become the most difficult problem to be solved. This article is based on the weather system in July 2, 2014, using the big data cloud technology to show the weather system. The diffusion of pollutants which in the same time was simulated by the CALPUFF model. And then, we can analyze the relationship between the weather system and regional pollutant dispersion.

Poverty alleviation and relocation is one of the three major measures for poverty alleviation and development, which help the relocation of the population to gradually get rid of poverty and get rich. The application of GIS technology of the poverty alleviation and relocation in the typical Karst mountain area is easy to find the appropriate relocation of residence, for the local government to provide decision-making reference. Resettlement area selection mainly consider the following factors: traffic facilities, the land is rich in resources, rich in water resources, gentle slope, lower elevation, avoid risks of geological disasters, natural protection area and national planning and construction land, ecological fragile area and the environmental carrying capacity overload region. Through the GIS technology, the above factor layer to find a suitable place to place. According to the placement of the situation to choose a suitable and easy to poverty alleviation and relocation model. The results showed that Pan County has a larger area of the resettlement area, There are four villages and towns belongs to the center village settlements, should use the center of the village resettlement mode; and nine villages and towns belongs to small urban settlements, urban commercial relocation mode should be adopted.

Flood hazard is one of the most common natural hazard in plain urban area. The location chosen for the study is Zhengzhou city, provincial capital, China. The model incorporates four factors: rainstorm with increased frequency and intensity, land subsidence, density of population, impermeable land surface, land use change. In this study, the coupling of geographical information system (GIS) and the analytical hierarchy process (AHP) were used, GIS analysis urban flood hazard vulnerability based on the underlying change and AHP was used in order to calculate the weighting values of each factor. A hazard map, based on the historical data in the past ten years, was obtained by spatial analysis technology. The hazard map was compared with the actual flood area, and good coincidence was found between them. The flood hazard assessment method presented here is meaningful for the local government to improve flood risk management and protecting environment.