nach oben

Earth Science Informatics

Erschienen in:

08.03.2019 | Review Article

Change detection techniques for remote sensing applications: a survey

verfasst von: Anju Asokan, J. Anitha

Erschienen in: Earth Science Informatics | Ausgabe 2/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Change detection captures the spatial changes from multi temporal satellite images due to manmade or natural phenomenon. It is of great importance in remote sensing, monitoring environmental changes and land use –land cover change detection. Remote sensing satellites acquire satellite images at varying resolutions and use these for change detection. This paper briefly analyses various change detection methods and the challenges and issues faced as part of change detection. Over the years, a wide range of methods have been developed for analyzing remote sensing data and newer methods are still being developed. Timely and accurate change detection of Earth’s surface features provides the basis for evaluating the relationships and interactions between human and natural phenomena for the better management of resources. In general, change detection applies multi-temporal datasets to quantitatively analyse the temporal effects of the phenomenon. As such, this study attempts to provide a comprehensive review of the fundamental processes required for change detection. The study also gives a brief account of the main techniques of change detection and discusses the need for development of enhanced change detection methods.

Nächster Artikel Force field driven skeleton extraction method for point cloud trees

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Ajadi O, Meyer F, Webley P (2016) Change detection in synthetic aperture radar images using a multiscale-driven approach. Remote Sens 8:482. https://doi.org/10.3390/rs8060482

Alonso-Montesinos J, Martínez-Durbán M, del Sagrado J, del Águila IM, Batlles FJ (2016) The application of Bayesian network classifiers to cloud classification in satellite images. Renew Energy 97:155–161. https://doi.org/10.1016/j.renene.2016.05.066

Alshehhi R, Marpu PR (2017) Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images. ISPRS J Photogramm Remote Sens 126:245–260

Amarnath G, Babar S, Sri M, Murthy R (2017) Evaluating MODIS-vegetation continuous field products to assess tree cover change and forest fragmentation in India – a multi-scale satellite remote sensing approach. Egypt J Remote Sensing Space Sci 20:157–168

Amici V, Marcantonio M, La Porta N, Rocchini D (2017) A multi-temporal approach in MaxEnt modelling: a new frontier for land use/land cover change detection. Ecol Inform 40:40–49. https://doi.org/10.1016/j.ecoinf.2017.04.005

Aslami F, Ghorbani A (2018) Object-based land-use / land-cover change detection using Landsat imagery : a case study of Ardabil , Namin , and Nir counties in Northwest Iran. Environ Monit Assess 190:1–14. https://doi.org/10.1007/s10661-018-6751-y

Azzouzi SA, Vidal-Pantaleoni A, Bentounes HA (2017) Desertification monitoring in Biskra, Algeria, with Landsat imagery by means of supervised classification and change detection methods. IEEE Access 5:9065–9072. https://doi.org/10.1109/ACCESS.2017.2700405

Barber J (2015) A generalized likelihood ratio test for coherent change detection in Polarimetric SAR. IEEE Geosci Remote Sens Lett 12:1873–1877. https://doi.org/10.1109/LGRS.2015.2433134

Berger A, Ettllin G, Quincke C, Rodriguez-Bocca P (2019) Predicting the normalized difference vegetation index(NDVI) by training a crop growth model with historical data. Comput Electron Agric:1–7

Bhandari AK, Soni V, Kumar A, Singh GK (2014) Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT – SVD. ISA Trans:1–11

Bhandari AK, Kumar A, Singh GK (2015a) Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Syst Appl 42:8707–8730

Bhandari AK, Kumar A, Singh GK (2015b) Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Syst Appl 42:1573–1601

Bose S, Mukherjee A, Madhulika, Chakraborty S, Samanta S, Dey N (2013) Parallel image segmentation using multi-threading and k-means algorithm. IEEE Int Conf Comput Intell Comput Res:1–5. https://doi.org/10.1109/ICCIC.2013.6724171

Cao G, Li X, Zhou L (2016a) Unsupervised change detection in high spatial resolution remote sensing images based on a conditional random field model. Eur J Remote Sens 49:225–237. https://doi.org/10.5721/EuJRS20164913

Cao G, Zhou L, Li Y (2016b) A new change-detection method in high-resolution remote sensing images based on a conditional random field model. Int J Remote Sens 37:1173–1189. https://doi.org/10.1080/01431161.2016.1148284

Chen T, Trinder JC, Niu R (2017) Object-oriented landslide mapping using ZY-3 satellite imagery, random forest and mathematical morphology, for the three-gorges reservoir, China. Remote Sens 9. https://doi.org/10.3390/rs9040333

Chen K, Fu K, Yan M, Gao X, Sun X, Wei X (2018) Semantic segmentation of aerial images with shuffling convolutional neural networks. IEEE Geosci Remote Sens Lett 15:173–177. https://doi.org/10.5194/isprs-annals-IV-1-29-2018

Chouhan S, Kaul A, Sharma U (2018) Soft computing approaches for image segmentation. Multimed Tools Appl 77:28483–28537

Cui B, Ma X, Xie X, Ren G, Ma Y (2017) Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering. Infrared Phys Technol 81:79–88. https://doi.org/10.1016/j.infrared.2016.12.010

Feizizadeh B, Blaschke T, Tiede D, Moghaddam MHR (2017) Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes. Geomorphology 293:240–254. https://doi.org/10.1016/j.geomorph.2017.06.002

Feng W, Sui H, Tu J, Huang W, Xu C, Sun K (2018) A novel change detection approach for multi-temporal high-resolution remote sensing images based on rotation forest and coarse-to-fine uncertainty analyses. Remote Sens 10. https://doi.org/10.3390/rs10071015

Ferraris V, Dobigeon N, Wei Q, Chabert M (2018) Detecting changes between optical images of different spatial and spectral resolutions: a fusion-based approach. IEEE Trans Geosci Remote Sens 56:1566–1578

Ferreira LS, Helena D, Duarte S (2019) Exploring the relationship between urban form , land surface temperature and vegetation indices in a subtropical megacity. Urban Clim 27:105–123

Fytsilis AL, Prokos A, Koutroumbas KD, Michail D, Kontoes CC (2016) A methodology for near real-time change detection between unmanned aerial vehicle and wide area satellite images. ISPRS J Photogramm Remote Sens 119:165–186. https://doi.org/10.1016/j.isprsjprs.2016.06.001

Gandhi M, Parthiban S, Thummalu N, Christy A (2015) Ndvi: vegetation change detection using remote sensing and gis – a case study of Vellore District. 3rd Int Conf Recent Trends Comput 57:1199–1210. https://doi.org/10.1016/j.procs.2015.07.415

Gandhimathi Alias Usha S, Vasuki S (2018) Improved segmentation and change detection of multi-spectral satellite imagery using graph cut based clustering and multiclass SVM. Multimed Tools Appl 77:15353–15383. https://doi.org/10.1007/s11042-017-5120-0

García P, Pérez E (2016) Mapping of soil sealing by vegetation indexes and built-up index : a case study in Madrid (Spain). Geoderma 268:100–107

Garcia-jimenez S, Jurio A, Pagola M, De Miguel L, Barrenechea E, Bustince H (2016) Forest fire detection: a fuzzy system approach based on overlap indices. Appl Soft Comput:1–9

Garzelli A, Aiazzi B, Alparone L, Lolli S, Vivone G (2018) Multispectral Pansharpening with radiative transfer-based detail-injection modeling for preserving changes in vegetation cover. Remote Sens 10:1308. https://doi.org/10.3390/rs10081308

Grinias I, Panagiotakis C, Tziritas G (2016) MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images. ISPRS J Photogramm Remote Sens 122:145–166

Gu W, Lv Z, Hao M (2017) Change detection method for remote sensing images based on an improved Markov random field. Multimed Tools Appl 76:17719–17734. https://doi.org/10.1007/s11042-015-2960-3

Han M, Zhou Y (2017) An adaptive unimodal subclass decomposition (AUSD) learning system for land use and land cover classification using high-resolution remote sensing. GIScience Remote Sens 54:20–37. https://doi.org/10.1080/15481603.2016.1246057

Hao M, Shi W, Deng K, Feng Q (2016) Superpixel-based active contour model for unsupervised change detection from satellite images. Int J Remote Sens 37:4276–4295. https://doi.org/10.1080/01431161.2016.1210838

Haque I, Basak R (2017) Land cover change detection using GIS and remote sensing techniques : a spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh. Egypt J Remote Sensing Space Sci 20:251–263

He P, Shi W, Zhang H, Hao M (2014) A novel dynamic threshold method for unsupervised change detection from remotely sensed images. Remote Sens Lett 5:396–403. https://doi.org/10.1080/2150704X.2014.912766

He P, Shi W, Miao Z, Zhang H, Cai L (2015) Advanced MarkRemote Sens Lettov random field model based on local uncertainty for unsupervised change detection. 6:667–676. https://doi.org/10.1080/2150704X.2015.1054045

Helmy AK, El-Taweel GS (2015) Image segmentation scheme based on SOM–PCNN in frequency domain. Appl Soft Comput J:1–11

Hölbling D, Friedl B, Eisank C (2015) An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan. Earth Sci Inf 8:327–335. https://doi.org/10.1007/s12145-015-0217-3

Holmström L, Pasanen L (2015) Bayesian scale space analysis of temporal changes in satellite images. J Appl Stat 42:50–70. https://doi.org/10.1080/02664763.2014.932761

Hore S, Chakraborty S, Chatterjee S, Dey N (2016) An integrated interactive technique for image segmentation using stack based seeded region growing and thresholding. Int J Electr Comput Eng 6:2773–2780. https://doi.org/10.11591/ijece.v6i6.11801

Hoseini P, Shayesteh MG (2013) Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing. Digit Signal Process 23:879–893

Huang B, Zhao B, Song Y (2018a) Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sens Environ 214:73–86

Huang F, Chen L, Yin K, Huang J, Gui L (2018b) Object-oriented change detection and damage assessment using high-resolution remote sensing images, Tangjiao landslide, three gorges reservoir, China. Environ Earth Sci 77:1–19. https://doi.org/10.1007/s12665-018-7334-5

Huang F, Yu Y, Feng T (2018c) Hyperspectral remote sensing image change detection based on tensor and deep learning. J Vis Commun Image Represent:2–24

Huang Z, Huang L, Li Q, Zhang T, Sang N (2018d) Framelet regularization for uneven intensity correction of color images with illumination and reflectance estimation. Neurocomputing:3–24. https://doi.org/10.1016/j.neucom.2018.06.063

Iino S, Ito R, Doi K, Imaizumi T, Hikosaka S (2018) CNN-based generation of high-accuracy urban distribution maps utilising SAR satellite imagery for short-term change monitoring. Int J Image Data Fusion 9832:1–17. https://doi.org/10.1080/19479832.2018.1491897

Jabari S, Zhang Y (2016) RPC-based Coregistration of VHR imagery for urban change detection. Photogramm Eng Remote Sens 82:521–534. https://doi.org/10.14358/PERS.82.7.521

Jiang L, Shang S, Yang Y, Guan H (2016) Mapping interannual variability of maize cover in a large irrigation district using a vegetation index – phenological index classifier. Comput Electron Agric 123:351–361

Johnson BA, Iizuka K, Bragais MA, Endo I, Magcale-macandog DB (2017) Employing crowdsourced geographic data and multi-temporal / multi-sensor satellite imagery to monitor land cover change : a case study in an urbanizing region of the Philippines. Comput Environ Urban Syst 64:184–193

Kaliraj S, Chandrasekar N, Ramachandran KK, Srinivas Y, Saravanan S (2017) Coastal landuse and land cover change and transformations of Kanyakumari coast , India using remote sensing and GIS. Egypt J Remote Sensing Space Sci 20:169–185

Kant K, Singh A (2017) Identification of flooded area from satellite images using hybrid Kohonen fuzzy C-means sigma classifier. Egypt J Remote Sensing Space Sci 20:147–155

Kapoor S, Zeya I, Singhal C, Nanda SJ (2017) A Grey Wolf Optimizer Based Automatic Clustering Algorithm for Satellite Image Segmentation. 7th Int Conf Adv Comput Commun ICACC-2017 115:415–22

Ke L, Lin Y, Zeng Z, Zhang L, Meng L (2018) Adaptive change detection with significance test. IEEE Access 6:27442–27450. https://doi.org/10.1109/ACCESS.2018.2807380

Kelly JT, Gontz AM (2018) Using GPS-surveyed intertidal zones to determine the validity of shorelines automatically mapped by Landsat water indices. Int J Appl Earth Obs Geoinf 65:92–104

Khairuzzaman AKM, Chaudhury S (2017) Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Syst Appl:3–34. https://doi.org/10.1016/j.eswa.2017.04.029

Khan SH, He X, Porikli F, Bennamoun M (2017) Forest change detection in incomplete satellite images with deep neural networks. IEEE Trans Geosci Remote Sens 55:5407–5423. https://doi.org/10.1109/TGRS.2017.2707528

Kleynhans W, Salmon BP, Olivier JC (2015) Detecting settlement expansion in South Africa using a hyper-temporal SAR change detection approach. Int J Appl Earth Obs Geoinf 42:142–149. https://doi.org/10.1016/j.jag.2015.06.004

Kleynhansa W, Salmon BP, Wessels KJ, Olivier JC (2015) Rapid detection of new and expanding human settlements in the Limpopo province of South Africa using a spatio-temporal change detection method. Int J Appl Earth Obs Geoinf 40:74–80. https://doi.org/10.1016/j.jag.2015.04.009

Kumar A, Kumar V, Kumar A, Kumar G (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41:3538–3560

Lei T, Xue D, Lv Z, Li S, Zhang Y, Nandi AK (2018) Unsupervised change detection using fast fuzzy clustering for landslide mapping from very high-resolution images. Remote Sens 10:1381. https://doi.org/10.3390/rs10091381

Li H, Gong M, Wang Q, Liu J, Su L (2015) A multiobjective fuzzy clustering method for change detection in SAR images. Appl Soft Comput:1–11

Li F, Zeng Y, Luo J, Ma R, Wu B (2016a) Modeling grassland aboveground biomass using a pure vegetation index. Ecol Indic 62:279–288

Li Z, Shi W, Myint SW, Lu P, Wang Q (2016b) Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method. Remote Sens Environ 175:215–230. https://doi.org/10.1016/j.rse.2016.01.003

Lin Y, Yu J, Cai J, Sneeuw N, Li F (2018) Spatio-temporal analysis of wetland changes using a kernel extreme learning machine approach. Remote Sens 10:1129. https://doi.org/10.3390/rs10071129

Liu S, Bruzzone L, Bovolo F, Zanetti M, Du P (2015) Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images. IEEE Trans Geosci Remote Sens 53:4363–4378. https://doi.org/10.1109/TGRS.2015.2396686

Liu C, Cheng I, Zhang Y, Basu A (2017) Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency. ISPRS J Photogramm Remote Sens 128:16–26

Liu J, Gong M, Qin K, Zhang P (2018a) A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE Trans Neural Networks Learn Syst 29:545–559. https://doi.org/10.1109/TNNLS.2016.2636227

Liu Q, Hang R, Song H, Li Z (2018b) Learning multiscale deep features for high-resolution satellite image scene classification. IEEE Trans Geosci Remote Sens 56:117–126. https://doi.org/10.1109/TGRS.2017.2743243

Liu T, Abd-Elrahman A, Zare A, Dewitt BA, Flory L, Smith SE (2018c) A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems. Remote Sens Environ 216:328–344. https://doi.org/10.1016/j.rse.2018.06.031

Liu Y, Ren Q, Geng J, Ding M (2018d) Efficient patch-wise semantic segmentation for large-scale remote sensing images. Sensors 18:1–16. https://doi.org/10.3390/s18103232

Liu Z, Li G, Mercier G, He Y, Pan Q (2018e) Change detection in Heterogenous remote sensing images via homogeneous pixel transformation. IEEE Trans Image Process 27:1822–1834. https://doi.org/10.1109/TIP.2017.2784560

Lu M, Hamunyela E, Verbesselt J, Pebesma E (2017) Dimension reduction of multi-spectral satellite image time series to improve deforestation monitoring. Remote Sens 9. https://doi.org/10.3390/rs9101025

Luo X, Zhang Z, Wu X (2016) A novel algorithm of remote sensing image fusion based on shift-invariant Shearlet transform and regional selection. Int J Electron Commun (AEÜ) 70:186–197

Luo H, Liu C, Wu C, Guo X (2018) Urban change detection based on Dempster–Shafer theory for multitemporal very high-resolution imagery. Remote Sens 10:980. https://doi.org/10.3390/rs10070980

Lv P, Zhong Y, Zhao J, Jiao H, Zhang L (2016) Change detection based on a multifeature probabilistic ensemble conditional random field model for high spatial resolution remote sensing imagery. IEEE Geosci Remote Sens Lett 13:1965–1969

Ma C, Xia W, Chen F, Liu J, Dai Q, Jiang L et al (2017) A content-based remote sensing image change information retrieval model. Isprs Int J Geo-Information 6:1–17. https://doi.org/10.3390/ijgi6100310

Ma Q, Su Y, Luo L, Li L, Kelly M, Guo Q (2018) Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data. Ecol Indic 95:298–310

Marinelli D, Bovolo F, Bruzzone L (2017) A novel method for unsupervised multiple change detection in hyperspectral images based on binary spectral change vectors. 2017 9th Int work anal multitemporal remote Sens images. MultiTemp 2017:1–4. https://doi.org/10.1109/Multi-Temp.2017.8035239

Marmanis D, Schindler K, Wegner JD, Galliani S, Datcu M, Stilla U (2018) Classification with an edge : improving semantic image segmentation with boundary detection. ISPRS J Photogramm Remote Sens 135:158–172

Massarelli C (2018) Fast detection of significantly transformed areas due to illegal waste burial with a procedure applicable to landsat images. Int J Remote Sens 39:754–769. https://doi.org/10.1080/01431161.2017.1390272

Miao Z, Fu K, Sun H, Sun X, Yan M (2018) Automatic water-body segmentation from high-resolution satellite images via deep networks. IEEE Geosci Remote Sens Lett 15:602–606. https://doi.org/10.1109/LGRS.2018.2794545

Minu S, Shetty A (2015) A Comparative Study of Image Change Detection Algorithms in MATLAB. Int. Conf. WATER Resour. Coast. Ocean Eng. (ICWRCOE 2015), Aquat. Procedia, vol. 4, p. 1366–73. https://doi.org/10.1016/j.aqpro.2015.02.177

Mittal H, Saraswat M (2018) An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm. Eng Appl Artif Intell 71:226–235

Mohammadi A, Costelloe JF, Ryu D (2017) Application of time series of remotely sensed normalized difference water , vegetation and moisture indices in characterizing flood dynamics of large-scale arid zone fl oodplains. Remote Sens Environ 190:70–82

Naidu MSR, Kumar PR, Chiranjeevi K (2017) Shannon and fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria Eng J:1–13

Narayan B, Bovolo F, Ghosh A, Bruzzone L (2014) Spatio-contextual fuzzy clustering with Markov random fi eld model for change detection in remotely sensed images. Opt Laser Technol 57:284–292

Pandey BK, Khare D (2017) Analyzing and modeling of a large river basin dynamics applying integrated cellular automata and Markov model. Environ Earth Sci 76:1–12. https://doi.org/10.1007/s12665-017-7133-4

Park H, Choi J, Park W, Park H (2018) Modified S2CVA algorithm using cross-sharpened images for unsupervised change detection. Sustainability 10:3301. https://doi.org/10.3390/su10093301

Patil SD, Gu Y, Dias FSA, Stieglitz M, Turk G (2017) Predicting the spectral information of future land cover using machine learning. Int J Remote Sens 38:5592–5607. https://doi.org/10.1080/01431161.2017.1343512

Pham LTH, Brabyn L (2017) Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms. ISPRS J Photogramm Remote Sens 128:86–97. https://doi.org/10.1016/j.isprsjprs.2017.03.013

Pradhan R, Aygun RS, Maskey M, Ramachandran R, Cecil DJ (2018) Tropical cyclone intensity estimation using a deep convolutional neural network. IEEE Trans Image Process 27:692–702. https://doi.org/10.1109/TIP.2017.2766358

Prakash S, Kumar A (2016) Evaluation of course change detection of Ramganga river using remote sensing and GIS, India. Weather Clim Extrem 13:68–72

Prendes J, Chabert M, Pascal F, Giros A, Tourneret J-Y (2015) A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors. IEEE Trans Image Process 24:799–812. https://doi.org/10.1109/TIP.2014.2387013

Qi Z, Yeh AG-O, Li X, Zhang X (2015) A three-component method for timely detection of land cover changes using polarimetric SAR images. ISPRS J Photogramm Remote Sens 107:3–21. https://doi.org/10.1016/j.isprsjprs.2015.02.004

Qin D, Zhou X, Zhou W, Huang G, Ren Y, Horan B et al (2018) MSIM: a change detection framework for damage assessment in natural disasters. Expert Syst Appl 97:372–383. https://doi.org/10.1016/j.eswa.2017.12.038

Qiu B, Chen G, Tang Z, Lu D, Wang Z, Chen C (2017) Assessing the three-north shelter Forest program in China by a novel framework for characterizing vegetation changes. ISPRS J Photogramm Remote Sens 133:75–88

R JVCI, Hagag A, Fan X, El-samie FEA (2017) HyperCast : hyperspectral satellite image broadcasting with band ordering optimization. J Vis Commun Image Represent 42:14–27

Radhika K, Varadarajan S (2018) A neural network based classification of satellite images for change detection applications. Cogent Eng 5:1–9

Rahbani M, Pakhirehzan M (2018) Classifying east coasts of Hormozgan province using Shepard method and satellite imagery. Egypt J Remote Sensing Space Sci 21:335–344. https://doi.org/10.1016/j.ejrs.2017.12.002

Rawat JS, Kumar M (2015) Monitoring land use / cover change using remote sensing and GIS techniques : a case study of Hawalbagh block , district Almora, Uttarakhand, India. Egypt J Remote Sensing Space Sci 18:77–84

Sadeghi V, Farnood Ahmadi F, Ebadi H (2016) Design and implementation of an expert system for updating thematic maps using satellite imagery (case study: changes of Lake Urmia). Arab J Geosci 9:1–17. https://doi.org/10.1007/s12517-015-2301-x

Sadeghi V, Farnood Ahmadi F, Ebadi H (2018) A new fuzzy measurement approach for automatic change detection using remotely sensed images. Meas J Int Meas Confed 127:1–14. https://doi.org/10.1016/j.measurement.2018.05.097

Salmon BP, Holloway DS, Kleynhans W, Olivier JC, Wessels KJ (2017) Applying model parameters as a driving force to a deterministic nonlinear system to detect land cover change. IEEE Trans Geosci Remote Sens 55:7165–7176. https://doi.org/10.1109/TGRS.2017.2743218

Sammouda R, Adgaba N, Touir A, Al-ghamdi A (2014) Agriculture satellite image segmentation using a modified artificial Hopfield neural network. Comput Hum Behav 30:436–441

Seydi ST, Hasanlou M (2018) Sensitivity analysis of pansharpening in hyperspectral change detection. Appl Geomatics 10:65–75. https://doi.org/10.1007/s12518-018-0206-6

Shakeri M, Dezfoulian MH, Khotanlou H, Barati AH, Masoumi Y (2016) Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization. Digit Signal Process 1:1–13

Shi A, Gao G, Shen S (2016) Change detection of bitemporal multispectral images based on FCM and D-S theory. EURASIP J Adv Signal Process 2016:1–12. https://doi.org/10.1186/s13634-016-0397-0

Singh A, Singh KK (2017) Satellite image classification using genetic algorithm trained radial basis function neural network, application to the detection of flooded areas. J Vis Commun Image Represent 42:173–181. https://doi.org/10.1016/j.jvcir.2016.11.017

Solano-Correa YT, Bovolo F, Bruzzone L (2018) An approach for unsupervised change detection in multitemporal VHR images acquired by different multispectral sensors. Remote Sens 10:1–23. https://doi.org/10.3390/rs10040533

Song W, Mu X, Ruan G, Gao Z, Li L, Yan G (2017) Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. Int J Appl Earth Obs Geoinf 58:168–176

Su L, Gong M, Zhang P, Zhang M, Liu J, Yang H (2017) Deep learning and mapping based ternary change detection for information unbalanced images. Pattern Recogn:2–42

Sumaiya MN, Kumari RSS (2018) Unsupervised change detection of flood affected areas in SAR images using Rayleigh based Bayesian thresholding. IET Radar, Sonar Navig 12:515–522. https://doi.org/10.1049/iet-rsn.2017.0393

Sumaiya MN, Shantha Selva Kumari R (2017a) Gabor filter based change detection in SAR images by KI thresholding. Optik (Stuttg) 130:114–122. https://doi.org/10.1016/j.ijleo.2016.11.040

Sumaiya MN, Shantha Selva Kumari R (2017b) Satellite image change detection using Laplacian–Gaussian distributions. Wirel Pers Commun 97:4621–4630. https://doi.org/10.1007/s11277-017-4741-y

Sun H, Wang Q, Wang G, Lin H, Luo P, Li J et al (2018) Optimizing kNN for mapping vegetation cover of arid and semi-arid areas using landsat images. Remote Sens 10. https://doi.org/10.3390/rs10081248

Suresh S, Lal S (2016) An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst Appl 58:184–209

Suresh S, Lal S (2017a) Modified differential evolution algorithm for contrast and brightness enhancement of satellite images. Appl Soft Comput J 61:622–641. https://doi.org/10.1016/j.asoc.2017.08.019

Suresh S, Lal S (2017b) Multilevel thresholding based on chaotic Darwinian particle swarm optimization for segmentation of satellite images. Appl Soft Comput:2–40. https://doi.org/10.1016/j.asoc.2017.02.005

Swain S, Abeysundara S, Hayhoe K, Stoner AMK (2017) Future changes in summer MODIS-based enhanced vegetation index for the south-Central United States. Ecol Inform:3–33. https://doi.org/10.1016/j.ecoinf.2017.07.007

Testa S, Soudani K, Boschetti L, Borgogno Mondino E, EVI MODIS-d (2018) NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests. Int J Appl Earth Obs Geoinf 64:132–144. https://doi.org/10.1016/j.jag.2017.08.006

Thakkar AK, Desai VR, Patel A, Potdar MB (2016) An effective hybrid classification approach using tasseled cap transformation (TCT) for improving classification of land use/land cover (LU/LC) in semi-arid region: a case study of Morva-Hadaf watershed, Gujarat, India. Arab J Geosci 9:1–13. https://doi.org/10.1007/s12517-015-2267-8

Thakkar AK, Desai VR, Patel A, Potdar MB (2017) Post-classification corrections in improving the classification of land use/land cover of arid region using RS and GIS: the case of Arjuni watershed, Gujarat, India. Egypt J Remote Sens Sp Sci 20:79–89. https://doi.org/10.1016/j.ejrs.2016.11.006

Tian D, Gong M (2018) A novel edge-weight based fuzzy clustering method for change detection in SAR images. Inf Sci (Ny) 467:415–430. https://doi.org/10.1016/j.ins.2018.08.015

Touati R, Mignotte M (2018) An energy-based model encoding nonlocal pairwise pixel interactions for multisensor change detection. IEEE Trans Geosci Remote Sens 56:1046–1058. https://doi.org/10.1109/TGRS.2017.2758359

Tuba M, Jordanski M, Arsic A (2016) Improved weighted thresholded histogram equalization algorithm for digital image contrast enhancement using the bat algorithm

Uchenna F, Timipre R, Chigozie E, Okpala-okaka C (2017) Geospatial assessment of vegetation status in Sagbama oilfield environment in the Niger Delta region, Nigeria. Egypt J Remote Sensing Space Sci 20:211–221

Vázquez-jiménez R, Romero-calcerrada R, Novillo CJ, Ramos-bernal RN, Arrogante-funes P (2017) Applying the chi-square transformation and automatic secant thresholding to Landsat imagery as unsupervised change detection methods. J Appl Remote Sens 11:016016(1-14). https://doi.org/10.1117/1.JRS.11.016016

Vicente-serrano SM, Camarero JJ, Olano JM, Martín-hernández N, Peña-gallardo M, Tomás-burguera M et al (2016) Diverse relationships between forest growth and the normalized difference vegetation index at a global scale. Remote Sens Environ 187:14–29

Vignesh T, Thyagharajan KK, Murugan D, Sakthivel M, Pushparaj S (2016) A novel multiple unsupervised algorithm for land use/land cover classification. Indian J Sci Technol 9:1–12. https://doi.org/10.17485/ijst/2016/v9i42/99682

Volpi M, Tuia D (2018) Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images. ISPRS J Photogramm Remote Sens 144:48–60

Wan X, Liu J, Li S, Dawson J, Yan H (2018) An illumination-invariant change detection method based on disparity saliency map for multitemporal optical remotely sensed images. IEEE Trans Geosci Remote Sens 99:1–14

Wang Q, Shi W, Atkinson PM, Li Z (2014) Land cover change detection at subpixel resolution with a Hopfield neural network. IEEE J Sel Top Appl Earth Obs Remote Sens 8:1339–1352. https://doi.org/10.1109/JSTARS.2014.2355832

Wang Y, Zhao F, Chen P (2017) A framework of spatiotemporal fuzzy clustering for land-cover change detection using SAR time series. Int J Remote Sens 38:450–466. https://doi.org/10.1080/01431161.2016.1268736

Wang Q, Zhang X, Chen G, Dai F, Gong Y, Zhu K (2018a) Change detection based on faster R-CNN for high-resolution remote sensing images. Remote Sens Lett 9:923–932. https://doi.org/10.1080/2150704X.2018.1492172

Wang X, Wang J, Che T, Huang X, Hao X, Li H (2018b) Snow cover mapping for complex mountainous forested environments based on a multi-index technique. IEEE J Sel Top Appl Earth Obs Remote Sens 11:1433–1441. https://doi.org/10.1109/JSTARS.2018.2810094

Xiao P, Zhang X, Wang D, Yuan M, Feng X, Kelly M (2016) Change detection of built-up land : a framework of combining pixel-based detection and object-based recognition. ISPRS J Photogramm Remote Sens 119:402–414

Xiong B, Chen JM, Kuang G (2012) A change detection measure based on a likelihood ratio and statistical properties of SAR intensity images. Remote Sens Lett 3:267–275. https://doi.org/10.1080/01431161.2011.572093

Xu D, Chen R, Xing X, Lin W (2017) Detection of decreasing vegetation cover based on empirical orthogonal function and temporal unmixing analysis. Math Probl Eng 2017:1–10. https://doi.org/10.1155/2017/5032091

Xue J, Su B (2017) Significant remote sensing vegetation indices : a review of developments and applications. J Sensors 2017:1–17

Yan L, Xia W, Zhao Z, Wang Y (2018) A novel approach to unsupervised change detection based on hybrid spectral difference. Remote Sens 10:1–21. https://doi.org/10.3390/rs10060841

Yang L, Jia K, Liang S, Wei X, Yao Y, Zhang X (2017) A robust algorithm for estimating surface fractional vegetation cover from landsat data. Remote Sens 9:1–20. https://doi.org/10.3390/rs9080857

Ye S, Rogan J, Sangermano F (2018) Monitoring rubber plantation expansion using Landsat data time series and a Shapelet-based approach. ISPRS J Photogramm Remote Sens 136:134–143

Yu X, Wu X, Luo C, Ren P (2017) Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. GIScience Remote Sens 54:741–758. https://doi.org/10.1080/15481603.2017.1323377

Yuan H, Wu C, Lu L, Wang X (2018) A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index. ISPRS J Photogramm Remote Sens 144:390–399

Zanchetta A, Bitelli G, Karnieli A (2016) Monitoring desertification by remote sensing using the Tasselled cap transform for long-term change detection. Nat Hazards 83:223–237. https://doi.org/10.1007/s11069-016-2342-9

Zanotta DC, Zortea M, Ferreira MP (2018) A supervised approach for simultaneous segmentation and classification of remote sensing images. ISPRS J Photogramm Remote Sens 142:162–173. https://doi.org/10.1016/j.isprsjprs.2018.05.021

Zhang P, Gong M, Su L, Liu J, Li Z (2016) Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images. ISPRS J Photogramm Remote Sens 116:24–41. https://doi.org/10.1016/j.isprsjprs.2016.02.013

Zhang P, Gong M, Su L, Liu J, Li Z (2017a) Feature learning and change feature classification based on deep learning for ternary change detection in SAR images. ISPRS J Photogramm Remote Sens 129:212–225. https://doi.org/10.1016/j.isprsjprs.2016.02.013

Zhang X, Xiao P, Feng X, Yuan M (2017b) Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area. Remote Sens Environ 201:243–255. https://doi.org/10.1016/j.rse.2017.09.022

Zhao B, Duan A, Ata-ul-karim ST, Liu Z, Chen Z, Gong Z et al (2018) Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. Eur J Agron 93:113–125

Zheng Z, Zeng Y, Li S, Huang W (2016) A new burn severity index based on land surface temperature and enhanced vegetation index. Int J Appl Earth Obs Geoinf 45:84–94

Zhuang H, Deng K, Yu Y, Fan H (2017) An approach based on discrete wavelet transform to unsupervised change detection in multispectral images. Int J Remote Sens 38:4914–4930. https://doi.org/10.1080/01431161.2017.1331475

Zhuang H, Fan H, Deng K, Yao G (2018) A spatial-temporal adaptive neighborhood-based ratio approach for change detection in SAR images. Remote Sens 10:1–19. https://doi.org/10.3390/rs10081295

Titel: Change detection techniques for remote sensing applications: a survey
verfasst von: Anju Asokan
J. Anitha
Publikationsdatum: 08.03.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 2/2019
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-019-00380-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2019

Correction to: An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Force field driven skeleton extraction method for point cloud trees

Minimizing the uncertainties of RCMs climate data by using spatio-temporal geostatistical modeling

Method for managing and querying geo-spatial data using a grid-code-array spatial index

Automatic seamless matching of area boundaries

Development of a machine vision system using the support vector machine regression (SVR) algorithm for the online prediction of iron ore grades

Premium Partner