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High-resolution remote sensing images (HRRSIs) contain substantial groundobject information, such as texture, shape, and spatial location. Semanticsegmentation, which is an important task for element extraction, has beenwidely used in processing mass HRRSIs. However, HRRSIs often exhibit largeintraclass variance and small interclass variance due to the diversity andcomplexity of ground objects, thereby bringing great challenges to a semanticsegmentation task. In this paper, we propose a new end-to-end semanticsegmentation network, which integrates lightweight spatial and channelattention modules that can refine features adaptively. We compare our methodwith several classic methods on the ISPRS Vaihingen and Potsdam datasets.Experimental results show that our method can achieve better semanticsegmentation results. The source codes are available athttps://github.com/lehaifeng/SCAttNet.

SCAttNet: Semantic Segmentation Network With Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images