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Feedback Attention-Based Dense CNN for Hyperspectral Image Classification
Hyperspectral image classification (HSIC) methods based on convolutional neural network (CNN) continue to progress in recent years. However, high complexity, information redundancy, and inefficient description still are the main barriers to the current HSIC networks. To address the mentioned problem...
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Published in: | IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-16 |
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description | Hyperspectral image classification (HSIC) methods based on convolutional neural network (CNN) continue to progress in recent years. However, high complexity, information redundancy, and inefficient description still are the main barriers to the current HSIC networks. To address the mentioned problems, we present a spatial-spectral dense CNN framework with a feedback attention mechanism called FADCNN for HSIC in this article. The proposed architecture assembles the spectral-spatial feature in a compact connection style to extract sufficient information independently with two separate dense CNN networks. Specifically, the feedback attention modules are developed for the first time to enhance the attention map with the semantic knowledge from the high-level layer of the dense model, and we strengthen the spatial attention module by considering multiscale spatial information. To further improve the computation efficiency and the discrimination of the feature representation, the band attention module is designed to emphasize the weight of the bands that participated in the classification training. Besides, the spatial-spectral features are integrated and mined intensely for better refinement in the feature mining network. The extensive experimental results on real hyperspectral images (HSI) demonstrate that the proposed FADCNN architecture has significant advantages compared with other state-of-the-art methods. |
doi_str_mv | 10.1109/TGRS.2021.3058549 |
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However, high complexity, information redundancy, and inefficient description still are the main barriers to the current HSIC networks. To address the mentioned problems, we present a spatial-spectral dense CNN framework with a feedback attention mechanism called FADCNN for HSIC in this article. The proposed architecture assembles the spectral-spatial feature in a compact connection style to extract sufficient information independently with two separate dense CNN networks. Specifically, the feedback attention modules are developed for the first time to enhance the attention map with the semantic knowledge from the high-level layer of the dense model, and we strengthen the spatial attention module by considering multiscale spatial information. To further improve the computation efficiency and the discrimination of the feature representation, the band attention module is designed to emphasize the weight of the bands that participated in the classification training. Besides, the spatial-spectral features are integrated and mined intensely for better refinement in the feature mining network. The extensive experimental results on real hyperspectral images (HSI) demonstrate that the proposed FADCNN architecture has significant advantages compared with other state-of-the-art methods.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2021.3058549</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial neural networks ; Attention map ; Classification ; Computation ; Computational modeling ; Computer architecture ; convolutional neural network (CNN) ; Data mining ; dense feature ; Feature extraction ; Feedback ; Frequency modulation ; hyperspectral image classification (HSIC) ; Hyperspectral imaging ; Image classification ; Information processing ; Methods ; Modules ; Neural networks ; Redundancy ; Spatial data ; spatial feature extraction ; Spectra ; spectral feature extraction ; Training</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-16</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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However, high complexity, information redundancy, and inefficient description still are the main barriers to the current HSIC networks. To address the mentioned problems, we present a spatial-spectral dense CNN framework with a feedback attention mechanism called FADCNN for HSIC in this article. The proposed architecture assembles the spectral-spatial feature in a compact connection style to extract sufficient information independently with two separate dense CNN networks. Specifically, the feedback attention modules are developed for the first time to enhance the attention map with the semantic knowledge from the high-level layer of the dense model, and we strengthen the spatial attention module by considering multiscale spatial information. To further improve the computation efficiency and the discrimination of the feature representation, the band attention module is designed to emphasize the weight of the bands that participated in the classification training. 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The extensive experimental results on real hyperspectral images (HSI) demonstrate that the proposed FADCNN architecture has significant advantages compared with other state-of-the-art methods.</description><subject>Artificial neural networks</subject><subject>Attention map</subject><subject>Classification</subject><subject>Computation</subject><subject>Computational modeling</subject><subject>Computer architecture</subject><subject>convolutional neural network (CNN)</subject><subject>Data mining</subject><subject>dense feature</subject><subject>Feature extraction</subject><subject>Feedback</subject><subject>Frequency modulation</subject><subject>hyperspectral image classification (HSIC)</subject><subject>Hyperspectral imaging</subject><subject>Image classification</subject><subject>Information processing</subject><subject>Methods</subject><subject>Modules</subject><subject>Neural networks</subject><subject>Redundancy</subject><subject>Spatial data</subject><subject>spatial feature extraction</subject><subject>Spectra</subject><subject>spectral feature extraction</subject><subject>Training</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNo9kMtOwzAQRS0EEqXwAYhNJNYpthO_lqXQUqkqEpS1ZTtjlNImwU4X_XsctWI1izn3zuggdE_whBCsnjaLj88JxZRMCswkK9UFGhHGZI55WV6iESaK51Qqeo1uYtxiTEpGxAgt5wCVNe4nm_Y9NH3dNvmziVBlL9BEyGbrdebbkL0dOwixA9cHs8uWe_OddjsTY-1rZ4bYLbryZhfh7jzH6Gv-upm95av3xXI2XeWOqqLPK5DCGi-9syCdscwTK4AAphYoK1n6qhKYYQ60tKC4dIWQoioqTKlXnhVj9Hjq7UL7e4DY6217CE06qSnHieWU0USRE-VCG2MAr7tQ7004aoL1YEwPxvRgTJ-NpczDKVMDwD-vCk5EavwDrvJnAA</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Yu, Chunyan</creator><creator>Han, Rui</creator><creator>Song, Meiping</creator><creator>Liu, Caiyu</creator><creator>Chang, Chein-I</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, high complexity, information redundancy, and inefficient description still are the main barriers to the current HSIC networks. To address the mentioned problems, we present a spatial-spectral dense CNN framework with a feedback attention mechanism called FADCNN for HSIC in this article. The proposed architecture assembles the spectral-spatial feature in a compact connection style to extract sufficient information independently with two separate dense CNN networks. Specifically, the feedback attention modules are developed for the first time to enhance the attention map with the semantic knowledge from the high-level layer of the dense model, and we strengthen the spatial attention module by considering multiscale spatial information. To further improve the computation efficiency and the discrimination of the feature representation, the band attention module is designed to emphasize the weight of the bands that participated in the classification training. 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subjects | Artificial neural networks Attention map Classification Computation Computational modeling Computer architecture convolutional neural network (CNN) Data mining dense feature Feature extraction Feedback Frequency modulation hyperspectral image classification (HSIC) Hyperspectral imaging Image classification Information processing Methods Modules Neural networks Redundancy Spatial data spatial feature extraction Spectra spectral feature extraction Training |
title | Feedback Attention-Based Dense CNN for Hyperspectral Image Classification |
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