A 3D-convolutional-autoencoder embedded Siamese-attention-network for classification of hyperspectral images
The classification of hyperspectral images (HSI) into categories that correlate to various land cover sorts such as water bodies, agriculture and urban areas, has gained significant attention in research due to its wide range of applications in fields, such as remote sensing, computer vision, and mo...
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| Published in: | Neural computing & applications Vol. 36; no. 15; pp. 8335 - 8354 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Springer London
01.05.2024
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | The classification of hyperspectral images (HSI) into categories that correlate to various land cover sorts such as water bodies, agriculture and urban areas, has gained significant attention in research due to its wide range of applications in fields, such as remote sensing, computer vision, and more. Supervised deep learning networks have demonstrated exceptional performance in HSI classification, capitalizing on their capacity for end-to-end optimization and leveraging their strong potential for nonlinear modeling. However, labelling HSIs, on the other hand, necessitates extensive domain knowledge and is a time-consuming and labour-intensive exercise. To address this issue, the proposed work introduces a novel semi-supervised network constructed with an autoencoder, Siamese action, and attention layers that achieves excellent classification accuracy with labelled limited samples. The proposed convolutional autoencoder is trained using the mass amount of unlabelled data to learn the refinement representation referred to as 3D-CAE. The added Siamese network improves the feature separability between different categories and attention layers improve classification by focusing on discriminative information and neglecting the unimportant bands. The efficacy of the proposed model’s performance was assessed by training and testing on both same-domain as well as cross-domain data and found to achieve 91.3 and 93.6 for Indian Pines and Salinas, respectively. |
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| AbstractList | The classification of hyperspectral images (HSI) into categories that correlate to various land cover sorts such as water bodies, agriculture and urban areas, has gained significant attention in research due to its wide range of applications in fields, such as remote sensing, computer vision, and more. Supervised deep learning networks have demonstrated exceptional performance in HSI classification, capitalizing on their capacity for end-to-end optimization and leveraging their strong potential for nonlinear modeling. However, labelling HSIs, on the other hand, necessitates extensive domain knowledge and is a time-consuming and labour-intensive exercise. To address this issue, the proposed work introduces a novel semi-supervised network constructed with an autoencoder, Siamese action, and attention layers that achieves excellent classification accuracy with labelled limited samples. The proposed convolutional autoencoder is trained using the mass amount of unlabelled data to learn the refinement representation referred to as 3D-CAE. The added Siamese network improves the feature separability between different categories and attention layers improve classification by focusing on discriminative information and neglecting the unimportant bands. The efficacy of the proposed model’s performance was assessed by training and testing on both same-domain as well as cross-domain data and found to achieve 91.3 and 93.6 for Indian Pines and Salinas, respectively. |
| Author | Girdhar, Ashish Kumar, Rajeev Ranjan, Pallavi |
| Author_xml | – sequence: 1 givenname: Pallavi surname: Ranjan fullname: Ranjan, Pallavi organization: Department of Computer Science, Delhi Technological University – sequence: 2 givenname: Rajeev orcidid: 0000-0002-5000-7644 surname: Kumar fullname: Kumar, Rajeev email: rajeevkumar@dtu.ac.in organization: Department of Computer Science, Delhi Technological University – sequence: 3 givenname: Ashish surname: Girdhar fullname: Girdhar, Ashish organization: Department of Computer Applications, Kurukshetra University |
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| SubjectTerms | Artificial Intelligence Artificial neural networks Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer vision Data Mining and Knowledge Discovery Hyperspectral imaging Image classification Image Processing and Computer Vision Land cover Machine learning Original Article Probability and Statistics in Computer Science Remote sensing |
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