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Accurate photovoltaic (PV) identification is critical for the effective and healthy development of PV industry. PV recognition is hampered by the complex background and variable shape and color of PV panels in high-resolution remote sensing images. This paper proposes a method for accurately extracting photovoltaic land from high-resolution remote sensing images. The encoder and decoder functions in this network combine multi-layer features to combine rich semantic data. Important spatial and channel properties are captured using the global convolution and the dual attention mechanism, while some lost channel data are recovered using the channel fusion module. The proposed method can effectively solve the problems of photovoltaic panel blurred edges and adhesion. Experiments on open PV datasets show that the IoU of the proposed method in PV01, PV03, and PV08 is 87.02%, 92.98%, and 88.43%, respectively, when compared to U-Net, SegNet, DeepLabv3, and DeepLabv3+. Experimental results show that the proposed method can achieve high accuracy segmentation of photovoltaic panels in high-resolution remote sensing images. [ABSTRACT FROM AUTHOR]