Photovoltaic land extraction from high-resolution remote sensing images based on deep learning method

doi:10.13474/j.cnki.11-2246.2021.0150

Abstract

Abstract: In recent years, the rapid development of China’s photovoltaic industry has caused a lot of land use problems. Extracting photovoltaic land through remote sensing technology and monitoring the distribution and status of photovoltaic land are of great significance for the healthy development of the photovoltaic industry. This research proposes a set of automatic photovoltaic land extraction methods for high-resolution remote sensing images. Satellite images such as GF-1 and Google Earth images are used to construct photovoltaic land samples. A deep learning semantic segmentation algorithm based on the ResNeSt-50 and DeepLab V3+model is proposed. The deep learning results are post-processed with computer graphic methods and the general-purpose and high-precision automatic extraction of photovoltaic land for high-resolution remote sensing images are achieved. The proposed deep learning model has a verification accuracy of mIoU of 0.899 2, and the extraction results have good edge accuracy. The method has wide applicability, and supports images such as GF-1, ZY-3, GF-6, GF-2, and Google Earth images.

Key words: GF satellite, photovoltaic land, deep learning, semantic segmentation, separate attention mechanism

CLC Number:

P237

WU Yongjing, WU Jinchao, LIN Chao, DOU Baocheng, LI Ke. Photovoltaic land extraction from high-resolution remote sensing images based on deep learning method[J]. Bulletin of Surveying and Mapping, 2021, 0(5): 96-101.

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