CFCANet: A Complete Frequency Channel Attention Network for SAR Image Scene Classification

An important means of remote sensing (RS) imagery interpretation, RS scene classification technology, has recently achieved great success, especially based on deep learning. However, most of these methods are designed for noise-free images. The scene classification performance for noisy RS images, i...

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Autores principales:	Bo Su, Jun Liu, Xin Su, Bin Luo, Qing Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Antinoise frequency attention mechanism synthetic aperture radar (SAR) image scene classification Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/8f059e91f99948b581362a8e334dbc4b
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id	oai:doaj.org-article:8f059e91f99948b581362a8e334dbc4b
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spelling	oai:doaj.org-article:8f059e91f99948b581362a8e334dbc4b2021-12-02T00:00:07ZCFCANet: A Complete Frequency Channel Attention Network for SAR Image Scene Classification2151-153510.1109/JSTARS.2021.3125107https://doaj.org/article/8f059e91f99948b581362a8e334dbc4b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9600873/https://doaj.org/toc/2151-1535An important means of remote sensing (RS) imagery interpretation, RS scene classification technology, has recently achieved great success, especially based on deep learning. However, most of these methods are designed for noise-free images. The scene classification performance for noisy RS images, i.e., synthetic aperture radar (SAR) images with speckle noise, is poor due to the sufficient effect of noise. An intuitive solution is denoising first and then classifying the image, which makes the whole process cumbersome. To address this problem, we design a new complete frequency channel attention network (CFCANet) that can handle noisy RS images directly without any filtering operation. CFCANet selects part of the low-frequency information to interact with the feature map adequately. For the original feature map, a corresponding 2-D discrete cosine transformation frequency component is assigned, from which the most significant eigenvalue of each channel is obtained by maximization. Compared with the frequency channel attention network (FcaNet), the proposed network has better antinoise ability as it exploits low frequency information of the images. The effectiveness of our method has been proved by experiments based on public datasets and some simulated datasets. Moreover, we build a new SAR scene classification dataset: WHU-SAR6. The comprehensive evaluation shows that the proposed method consistently outperforms several advanced methods, including ResNet, SENet, CBAM, EcaNet, and FcaNet.Bo SuJun LiuXin SuBin LuoQing WangIEEEarticleAntinoisefrequency attention mechanismsynthetic aperture radar (SAR) image scene classificationOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11750-11763 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Antinoise frequency attention mechanism synthetic aperture radar (SAR) image scene classification Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Antinoise frequency attention mechanism synthetic aperture radar (SAR) image scene classification Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Bo Su Jun Liu Xin Su Bin Luo Qing Wang CFCANet: A Complete Frequency Channel Attention Network for SAR Image Scene Classification
description	An important means of remote sensing (RS) imagery interpretation, RS scene classification technology, has recently achieved great success, especially based on deep learning. However, most of these methods are designed for noise-free images. The scene classification performance for noisy RS images, i.e., synthetic aperture radar (SAR) images with speckle noise, is poor due to the sufficient effect of noise. An intuitive solution is denoising first and then classifying the image, which makes the whole process cumbersome. To address this problem, we design a new complete frequency channel attention network (CFCANet) that can handle noisy RS images directly without any filtering operation. CFCANet selects part of the low-frequency information to interact with the feature map adequately. For the original feature map, a corresponding 2-D discrete cosine transformation frequency component is assigned, from which the most significant eigenvalue of each channel is obtained by maximization. Compared with the frequency channel attention network (FcaNet), the proposed network has better antinoise ability as it exploits low frequency information of the images. The effectiveness of our method has been proved by experiments based on public datasets and some simulated datasets. Moreover, we build a new SAR scene classification dataset: WHU-SAR6. The comprehensive evaluation shows that the proposed method consistently outperforms several advanced methods, including ResNet, SENet, CBAM, EcaNet, and FcaNet.
format	article
author	Bo Su Jun Liu Xin Su Bin Luo Qing Wang
author_facet	Bo Su Jun Liu Xin Su Bin Luo Qing Wang
author_sort	Bo Su
title	CFCANet: A Complete Frequency Channel Attention Network for SAR Image Scene Classification
title_short	CFCANet: A Complete Frequency Channel Attention Network for SAR Image Scene Classification
title_full	CFCANet: A Complete Frequency Channel Attention Network for SAR Image Scene Classification
title_fullStr	CFCANet: A Complete Frequency Channel Attention Network for SAR Image Scene Classification
title_full_unstemmed	CFCANet: A Complete Frequency Channel Attention Network for SAR Image Scene Classification
title_sort	cfcanet: a complete frequency channel attention network for sar image scene classification
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8f059e91f99948b581362a8e334dbc4b
work_keys_str_mv	AT bosu cfcanetacompletefrequencychannelattentionnetworkforsarimagesceneclassification AT junliu cfcanetacompletefrequencychannelattentionnetworkforsarimagesceneclassification AT xinsu cfcanetacompletefrequencychannelattentionnetworkforsarimagesceneclassification AT binluo cfcanetacompletefrequencychannelattentionnetworkforsarimagesceneclassification AT qingwang cfcanetacompletefrequencychannelattentionnetworkforsarimagesceneclassification
_version_	1718403998255415296

CFCANet: A Complete Frequency Channel Attention Network for SAR Image Scene Classification

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