SCAttNet: Semantic Segmentation Network With Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images
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...
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| Published in | IEEE geoscience and remote sensing letters Vol. 18; no. 5; pp. 905 - 909 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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 . |
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| AbstractList | 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 . |
| Author | Qiu, Kaijian Hong, Liang Tao, Chao Li, Haifeng Chen, Li Mei, Xiaoming |
| Author_xml | – sequence: 1 givenname: Haifeng orcidid: 0000-0003-1173-6593 surname: Li fullname: Li, Haifeng organization: School of Geosciences and Info-Physics, Central South University, Changsha, China – sequence: 2 givenname: Kaijian surname: Qiu fullname: Qiu, Kaijian organization: School of Geosciences and Info-Physics, Central South University, Changsha, China – sequence: 3 givenname: Li orcidid: 0000-0002-4761-5913 surname: Chen fullname: Chen, Li organization: School of Geosciences and Info-Physics, Central South University, Changsha, China – sequence: 4 givenname: Xiaoming surname: Mei fullname: Mei, Xiaoming organization: School of Geosciences and Info-Physics, Central South University, Changsha, China – sequence: 5 givenname: Liang surname: Hong fullname: Hong, Liang organization: College of Tourism and Geographic Science, Yunnan Normal University, Kunming, China – sequence: 6 givenname: Chao orcidid: 0000-0003-0071-310X surname: Tao fullname: Tao, Chao email: kingtaohao@csu.edu.cn organization: School of Geosciences and Info-Physics, Central South University, Changsha, China |
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| SubjectTerms | Attention module Computational modeling convolutional neural network Feature extraction High resolution Image resolution Image segmentation Remote sensing Resolution Semantic segmentation Semantics Task analysis Training |
| Title | SCAttNet: Semantic Segmentation Network With Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images |
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