Water information extraction from high-resolution remote sensing images using the deep-learning based semantic segmentation model
SHEN Jun’ao1(), MA Mengting2, SONG Zhiyuan1, LIU Tingzhou1, ZHANG Wei1,2()
1. School of Software Technology, Zhejiang University, Ningbo 315048, China 2. School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Water information extraction is an important study direction in the application of high spatial resolution remote sensing images. Conventional recognition methods only focus on the shallow features of water. Therefore, to further improve the robustness of water information extraction algorithms and increase the segmentation precision by extracting more deep information from remote sensing images, this study proposed a water classification method using the semantic segmentation model based on deep learning. First, deep neural networks were used to mine the information from high-resolution remote sensing images. Then, attention modules were used to integrate the deep information with the shallow features such as shape, structure, texture, and hue. Based on the integrated information, a new deep semantic segmentation model with higher precision and prediction efficiency than existent models was built. Finally, the ablation experiment was conducted to compare with conventional recognition methods and common semantic segmentation models. The experiment demonstrates that the proposed algorithm model yields higher overall precision and efficiency than previous methods and that the algorithm parameters are easy to set and less human intervention is required in the model. This study proved the accuracy and efficiency of deep learning and attention mechanism on water information extraction from high-resolution remote sensing images. Moreover, this study provided a possible solution for the segmentation of high-resolution remote sensing images using the deep learning method and explored the future prospect of the solution.
沈骏翱, 马梦婷, 宋致远, 柳汀洲, 张微. 基于深度学习语义分割模型的高分辨率遥感图像水体提取[J]. 自然资源遥感, 2022, 34(4): 129-135.
SHEN Jun’ao, MA Mengting, SONG Zhiyuan, LIU Tingzhou, ZHANG Wei. Water information extraction from high-resolution remote sensing images using the deep-learning based semantic segmentation model. Remote Sensing for Natural Resources, 2022, 34(4): 129-135.
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2022, Vol. 34 
), 马梦婷2, 宋致远1, 柳汀洲1, 张微1,2(
