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

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 in...