Superpixel guided deep-sparse-representation learning for hyperspectral image classification
This paper presents a new technique for hyperspectral image (HSI) classification by using superpixel guided deep-sparse-representation learning. The proposed technique constructs a hierarchical architecture by exploiting the sparse coding to learn the HSI representation. Specifically, a multiple-lay...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/142926 |
| _version_ | 1826125498259865600 |
|---|---|
| author | Fan, Jiayuan Chen, Tao Lu, Shijian |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Fan, Jiayuan Chen, Tao Lu, Shijian |
| author_sort | Fan, Jiayuan |
| collection | NTU |
| description | This paper presents a new technique for hyperspectral image (HSI) classification by using superpixel guided deep-sparse-representation learning. The proposed technique constructs a hierarchical architecture by exploiting the sparse coding to learn the HSI representation. Specifically, a multiple-layer architecture using different superpixel maps is designed, where each superpixel map is generated by downsampling the superpixels gradually along with enlarged spatial regions for labeled samples. In each layer, sparse representation of pixels within every spatial region is computed to construct a histogram via the sum-pooling with $l-{1}$ normalization. Finally, the representations (features) learned from the multiple-layer network are aggregated and trained by a support vector machine classifier. The proposed technique has been evaluated over three public HSI data sets, including the Indian Pines image set, the Salinas image set, and the University of Pavia image set. Experiments show superior performance compared with the state-of-the-art methods. |
| first_indexed | 2024-10-01T06:37:36Z |
| format | Journal Article |
| id | ntu-10356/142926 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:37:36Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1429262020-07-14T01:22:55Z Superpixel guided deep-sparse-representation learning for hyperspectral image classification Fan, Jiayuan Chen, Tao Lu, Shijian School of Computer Science and Engineering Engineering::Computer science and engineering Hyperspectral Classification This paper presents a new technique for hyperspectral image (HSI) classification by using superpixel guided deep-sparse-representation learning. The proposed technique constructs a hierarchical architecture by exploiting the sparse coding to learn the HSI representation. Specifically, a multiple-layer architecture using different superpixel maps is designed, where each superpixel map is generated by downsampling the superpixels gradually along with enlarged spatial regions for labeled samples. In each layer, sparse representation of pixels within every spatial region is computed to construct a histogram via the sum-pooling with $l-{1}$ normalization. Finally, the representations (features) learned from the multiple-layer network are aggregated and trained by a support vector machine classifier. The proposed technique has been evaluated over three public HSI data sets, including the Indian Pines image set, the Salinas image set, and the University of Pavia image set. Experiments show superior performance compared with the state-of-the-art methods. 2020-07-14T01:22:55Z 2020-07-14T01:22:55Z 2017 Journal Article Fan, J., Chen, T., & Lu, S. (2018). Superpixel guided deep-sparse-representation learning for hyperspectral image classification. IEEE Transactions on Circuits and Systems for Video Technology, 28(11), 3163-3173. doi:10.1109/TCSVT.2017.2746684 1051-8215 https://hdl.handle.net/10356/142926 10.1109/TCSVT.2017.2746684 2-s2.0-85028730379 11 28 3163 3173 en IEEE Transactions on Circuits and Systems for Video Technology © 2017 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Hyperspectral Classification Fan, Jiayuan Chen, Tao Lu, Shijian Superpixel guided deep-sparse-representation learning for hyperspectral image classification |
| title | Superpixel guided deep-sparse-representation learning for hyperspectral image classification |
| title_full | Superpixel guided deep-sparse-representation learning for hyperspectral image classification |
| title_fullStr | Superpixel guided deep-sparse-representation learning for hyperspectral image classification |
| title_full_unstemmed | Superpixel guided deep-sparse-representation learning for hyperspectral image classification |
| title_short | Superpixel guided deep-sparse-representation learning for hyperspectral image classification |
| title_sort | superpixel guided deep sparse representation learning for hyperspectral image classification |
| topic | Engineering::Computer science and engineering Hyperspectral Classification |
| url | https://hdl.handle.net/10356/142926 |
| work_keys_str_mv | AT fanjiayuan superpixelguideddeepsparserepresentationlearningforhyperspectralimageclassification AT chentao superpixelguideddeepsparserepresentationlearningforhyperspectralimageclassification AT lushijian superpixelguideddeepsparserepresentationlearningforhyperspectralimageclassification |