Spectral Partitioning Residual Network With Spatial Attention Mechanism for Hyperspectral Image Classification
Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral data analysis. Convolutional neural networks (CNN) have been introduced to HSI classification and achieved good performance. In this article, an effective and efficient CNN-based spectral partitioning residu...
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| Vydané v: | IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 14 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0196-2892, 1558-0644 |
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| Abstract | Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral data analysis. Convolutional neural networks (CNN) have been introduced to HSI classification and achieved good performance. In this article, an effective and efficient CNN-based spectral partitioning residual network (SPRN) is proposed for HSI classification. The SPRN splits the input spectral bands into several nonoverlapping continuous subbands and uses cascaded parallel improved residual blocks to extract spectral-spatial features from these subbands, respectively. Finally, the features are fused and fed into a classifier. By equivalently using grouped convolutions, the spectral partition and feature extraction are embedded into an end-to-end network. Experimental results show that the proposed SPRN achieves state-of-the-art performance, meanwhile, with relatively fewer parameters and computational costs. Usually, the CNN takes a patch that contains continuous spatial information as the input and results in a class label of the center pixel. The large size of the input patch includes more spatial information, whereas also introduces interfering pixels that may lead to a degradation of classification accuracies. For that reason, we propose a novel spatial attention module named homogeneous pixel detection module (HPDM). The module alleviates the degradation of performance as the input patch size increases by capturing the homogeneous pixels in the input patch. The module can be integrated into any CNN-based HSI classification framework. |
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| AbstractList | Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral data analysis. Convolutional neural networks (CNN) have been introduced to HSI classification and achieved good performance. In this article, an effective and efficient CNN-based spectral partitioning residual network (SPRN) is proposed for HSI classification. The SPRN splits the input spectral bands into several nonoverlapping continuous subbands and uses cascaded parallel improved residual blocks to extract spectral-spatial features from these subbands, respectively. Finally, the features are fused and fed into a classifier. By equivalently using grouped convolutions, the spectral partition and feature extraction are embedded into an end-to-end network. Experimental results show that the proposed SPRN achieves state-of-the-art performance, meanwhile, with relatively fewer parameters and computational costs. Usually, the CNN takes a patch that contains continuous spatial information as the input and results in a class label of the center pixel. The large size of the input patch includes more spatial information, whereas also introduces interfering pixels that may lead to a degradation of classification accuracies. For that reason, we propose a novel spatial attention module named homogeneous pixel detection module (HPDM). The module alleviates the degradation of performance as the input patch size increases by capturing the homogeneous pixels in the input patch. The module can be integrated into any CNN-based HSI classification framework. |
| Author | Shang, Shouwang Tang, Xu Feng, Jie Zhang, Xiangrong Jiao, Licheng |
| Author_xml | – sequence: 1 givenname: Xiangrong orcidid: 0000-0003-0379-2042 surname: Zhang fullname: Zhang, Xiangrong email: xrzhang@mail.xidian.edu.cn organization: School of Artificial Intelligence, Xidian University, Xi'an, China – sequence: 2 givenname: Shouwang surname: Shang fullname: Shang, Shouwang email: swshang@stu.xidian.edu.cn organization: School of Artificial Intelligence, Xidian University, Xi'an, China – sequence: 3 givenname: Xu orcidid: 0000-0003-1375-0778 surname: Tang fullname: Tang, Xu organization: School of Artificial Intelligence, Xidian University, Xi'an, China – sequence: 4 givenname: Jie orcidid: 0000-0002-8032-7542 surname: Feng fullname: Feng, Jie organization: School of Artificial Intelligence, Xidian University, Xi'an, China – sequence: 5 givenname: Licheng orcidid: 0000-0003-3354-9617 surname: Jiao fullname: Jiao, Licheng organization: School of Artificial Intelligence, Xidian University, Xi'an, China |
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| SubjectTerms | Artificial neural networks Classification Computer applications Correlation Data analysis Data mining Degradation Feature extraction Grouped convolutions hyperspectral image (HSI) classification Hyperspectral imaging Image classification Iron Modules Neural networks Partitioning Performance degradation Pixels Residual neural networks spatial attention mechanism Spatial data Spectra Spectral bands spectral partition (SP) |
| Title | Spectral Partitioning Residual Network With Spatial Attention Mechanism for Hyperspectral Image Classification |
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