Hyperspectral Image Classification Based on Stacked Contractive Autoencoder Combined With Adaptive Spectral-Spatial Information
Hyperspectral image (HSI) contain abundant spectral and spatial information, enabling the accurate classification of ground objects. However, many existing machine learning methods have poor performance, and some existing CNN-based methods require high computational power, which considerably limits...
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| Vydáno v: | IEEE access Ročník 9; s. 96404 - 96415 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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2021
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | Hyperspectral image (HSI) contain abundant spectral and spatial information, enabling the accurate classification of ground objects. However, many existing machine learning methods have poor performance, and some existing CNN-based methods require high computational power, which considerably limits their real-world applications. To address these issues, in this paper, we propose an alternative HSI classification method based on the stacked contractive autoencoder (SCAE) and adaptive spectral-spatial information to improve the accuracy of HSI classification. Specifically, the non-subsampled shearlet transform (NSST) with the guided filtering (NG) enhances spatial structure information. Subsequently, we present an adaptive spatial information extraction method to extract the spatial information of pixels. Furthermore, we propose an HSI classification network, called SCAE-LR, for feature extraction and classification. The SCAE is implemented to extract the adaptive spectral-spatial feature, and a logistic regression (LR) layer is employed for classification. Extensive experiments on the Indian Pines data set and the Pavia University data set demonstrate the superior performance of our method. |
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| AbstractList | Hyperspectral image (HSI) contain abundant spectral and spatial information, enabling the accurate classification of ground objects. However, many existing machine learning methods have poor performance, and some existing CNN-based methods require high computational power, which considerably limits their real-world applications. To address these issues, in this paper, we propose an alternative HSI classification method based on the stacked contractive autoencoder (SCAE) and adaptive spectral-spatial information to improve the accuracy of HSI classification. Specifically, the non-subsampled shearlet transform (NSST) with the guided filtering (NG) enhances spatial structure information. Subsequently, we present an adaptive spatial information extraction method to extract the spatial information of pixels. Furthermore, we propose an HSI classification network, called SCAE-LR, for feature extraction and classification. The SCAE is implemented to extract the adaptive spectral-spatial feature, and a logistic regression (LR) layer is employed for classification. Extensive experiments on the Indian Pines data set and the Pavia University data set demonstrate the superior performance of our method. |
| Author | Guo, Pengyue Wang, Zimin Liu, Zhenbing Lu, Haoxiang |
| Author_xml | – sequence: 1 givenname: Pengyue orcidid: 0000-0002-5528-4923 surname: Guo fullname: Guo, Pengyue organization: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China – sequence: 2 givenname: Zhenbing surname: Liu fullname: Liu, Zhenbing email: zbliu2011@163.com organization: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China – sequence: 3 givenname: Haoxiang orcidid: 0000-0003-2284-5154 surname: Lu fullname: Lu, Haoxiang organization: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China – sequence: 4 givenname: Zimin surname: Wang fullname: Wang, Zimin organization: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China |
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| SubjectTerms | adaptive spectral-spatial information Adaptive systems Classification Data mining Datasets Feature extraction Hyperspectral image Hyperspectral imaging Image classification Image reconstruction Information retrieval logistic regression Logistics Machine learning Spatial data Spectra Transforms |
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| Title | Hyperspectral Image Classification Based on Stacked Contractive Autoencoder Combined With Adaptive Spectral-Spatial Information |
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