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2023 | OriginalPaper | Chapter

Application and Prospect of Artificial Intelligence Image Analysis Technology in Natural Resources Survey

Authors : Yuehong Wang, Hao Luo

Published in: Artificial Intelligence in China

Publisher: Springer Nature Singapore

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Abstract

With the rapid development of artificial intelligence, the application of Computer vision and Machine learning in the field of photogrammetry and remote sensing continues to enrich. Cognitive reasoning based on spatiotemporal big data has gradually deepened, which greatly promotes the development of remote sensing and surveying and mapping geographic information technology. The natural resource survey and monitoring technology system presents the characteristics of intelligence, spatialization, ubiquity and multi-source, which promotes the development of natural resource survey and monitoring business.

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previous chapter Research on the Service for the Disabled in Interior Design and Product Design Based on Artificial Intelligence
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Literature
1.
Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: Sigspatial International Conference on Advances in Geographic Information Systems, p. 270. ACM (2010)
2.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)CrossRef
3.
Bei, Z., Zhong, Y., Zhang, L., et al.: The fisher kernel coding framework for high spatial resolution scene classification. Remote Sens. 8(2), 157 (2016)CrossRef
4.
Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007. IEEE (2007)
5.
Negrel, R., Picard, D., Gosselin, P.H.: Evaluation of second-order visual features for land-use classification. In: International Workshop on Content-based Multimedia Indexing. IEEE (2014)
6.
Cheng, G., Han, J., Guo, L., et al.: Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE Trans. Geosci. Remote Sens. 53(8), 4238–4249 (2015)CrossRef
7.
Dang, Y., Zhang, J., Deng, K., Zhao, Y., Yu, F.: Study on the evaluation of land cover classification using remote sensing images based on AlexNet. J. Geo-inf. Sci. 19(11), 1530–1537 (2017)
8.
Wang, X., Li, K., Ning, C., Huang, F.: Remote sensing image classification method based on deep convolution neural network and multi-kernel learning. J. Electron. Inf. Technol. 41(5), 1098–1105 (2019)
9.
Lin, H., Shi, Z., Zou, Z.: Fully convolutional network with task partitioning for inshore ship detection in optical remote sensing images. IEEE Geosci. Remote Sens. Lett. 14(10), 1665–1669 (2017)CrossRef
10.
Chen, G., Zhang, X., Wang, Q., et al.: Symmetrical dense-shortcut deep fully convolutional networks for semantic segmentation of very-high-resolution remote sensing images. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 11, 1633–1644 (2018)CrossRef
11.
Sun, W., Wang, R.: Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM. IEEE Geosci. Remote Sens. Lett. 15, 1–5 (2018)CrossRef
12.
Maggiori, E., Tarabalka, Y., Charpiat, G., et al.: Fully convolutional neural networks for remote sensing image classification. In: Geoscience & Remote Sensing Symposium. IEEE (2016)
13.
Chen, G., Li, C., Wei, W., et al.: Fully convolutional neural network with augmented atrous spatial pyramid pool and fully connected fusion path for high resolution remote sensing image segmentation. Appl. Sci. 9(9), 1816 (2019)CrossRef
14.
Zhang, K., Guo, Y., Wang, X., et al.: Multiple feature reweight densenet for image classification. IEEE Access 7, 9872–9880 (2019)CrossRef
Metadata
Title
Application and Prospect of Artificial Intelligence Image Analysis Technology in Natural Resources Survey
Authors
Yuehong Wang
Hao Luo
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Artificial Intelligence in China

Print ISBN: 978-981-9912-55-1

Electronic ISBN: 978-981-9912-56-8

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-99-1256-8_18

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