@inproceedings{e07f6b9633df4c11a984850fc82f5870,
title = "CSAM: A Channel and Spatial Attention Mechanism for Impervious Surface Extraction in Difficult Areas",
abstract = "Impervious surface extraction from remote sensing images has become a promising technology to measure the urban ecological environment and monitor human activity. However, due to the complex characteristics of impervious landscapes, most researches on impervious surface extraction hardly identify the scattered and small objects especially in difficult areas, which severely affect the accuracy of mapping impervious surface. In this work, we propose a channel and spatial attention mechanism (CSAM) to extract impervious surface in difficult areas, which includes a channel attention module to learn the relationship in the multi-channel remote sensing images and a spatial attention module to capture the features of the inconspicuous objects. Experiments with the Sentinel-2 dataset in South Africa demonstrate that CSAM can outperform the state-of-the-art methods.",
keywords = "Channel and spatial attention, Deep learning, Impervious surface extraction, Semantic segmentation",
author = "Fangyuan Zhao and Xiaohua Wan and Sijia Li and Zhongchang Sun and Wenjing Zhang and Dehui Qiu and Fa Zhang and Xinyu Liu and Guangming Tan",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",
year = "2022",
doi = "10.1109/IGARSS46834.2022.9884449",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1360--1363",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
}