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

• Published in: IEEE geoscience and remote sensing letters Vol. 18; no. 5; pp. 905 - 909
• Main Authors: Li, Haifeng, Qiu, Kaijian, Chen, Li, Mei, Xiaoming, Hong, Liang, Tao, Chao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Attention module; Computational modeling; convolutional neural network; Feature extraction; High resolution; Image resolution; Image segmentation; Remote sensing; Resolution; Semantic segmentation; Semantics; Task analysis; Training
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2020.2988294

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this letter, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam data sets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet .