Typical scene material classification based on remote sensing image data

Yang Gao; Ling Dai; Haipeng Li; Yini Zhang; Feng Wang

doi:10.1117/12.3012646

7 March 2024 Typical scene material classification based on remote sensing image data

Yang Gao, Ling Dai, Haipeng Li, Yini Zhang, Feng Wang

Author Affiliations +

Proceedings Volume 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 130880D (2024) https://doi.org/10.1117/12.3012646
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

In spectral scene simulation applications, it is necessary to recognize and detect desert satellite images and satellite images containing water. However, previous network models such as AlexNet, VggNet, and GoogleNet have gradually fallen behind the trend of the times. Compared with the latest network models, they not only have slow training speeds but also do not achieve high accuracy. Therefore, this study adopts a series of the latest network models to solve this problem and compare and analyze the results.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yang Gao, Ling Dai, Haipeng Li, Yini Zhang, and Feng Wang "Typical scene material classification based on remote sensing image data", Proc. SPIE 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 130880D (7 March 2024); https://doi.org/10.1117/12.3012646

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