Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples
The classification of hyperspectral images is one of the most popular fields in remote sensing applications. It should be noted that spectral and spatial features have critical roles in this research area. This paper proposes a method based on spatial-spectral Schroedinger eigenmaps (SSSE) and multi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-09-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2021.1978840 |
| _version_ | 1797661111446667264 |
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| author | Shirin Hassanzadeh Habibollah Danyali Mohammad Sadegh Helfroush |
| author_facet | Shirin Hassanzadeh Habibollah Danyali Mohammad Sadegh Helfroush |
| author_sort | Shirin Hassanzadeh |
| collection | DOAJ |
| description | The classification of hyperspectral images is one of the most popular fields in remote sensing applications. It should be noted that spectral and spatial features have critical roles in this research area. This paper proposes a method based on spatial-spectral Schroedinger eigenmaps (SSSE) and multiple kernel learning (MKL) to classify hyperspectral images more efficiently while using a low number of training samples. In the proposed method, first SSSE is applied to spectral domain in order to extract significant features and reduce dimension of the original image. Then MKL is utilized to enhance the feature learning process and obtain an optimum combination of some specified kernels. Finally, the classification is carried out by substituting the optimal kernel in support vector machine (SVM) algorithm. Experimental results show that the proposed method improves classification accuracy significantly and provides highly efficient results in the case of a small number of training samples. Furthermore, the computation time of the proposed method is much lower than the state-of-the-art MKL methods. |
| first_indexed | 2024-03-11T18:39:43Z |
| format | Article |
| id | doaj.art-53817fe279db43618e4ef6e4dbe6c2b7 |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:39:43Z |
| publishDate | 2022-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-53817fe279db43618e4ef6e4dbe6c2b72023-10-12T13:36:24ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712022-09-0148557959110.1080/07038992.2021.19788401978840Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training SamplesShirin Hassanzadeh0Habibollah Danyali1Mohammad Sadegh Helfroush2Department of Electrical and Electronics Engineering, Shiraz University of TechnologyDepartment of Electrical and Electronics Engineering, Shiraz University of TechnologyDepartment of Electrical and Electronics Engineering, Shiraz University of TechnologyThe classification of hyperspectral images is one of the most popular fields in remote sensing applications. It should be noted that spectral and spatial features have critical roles in this research area. This paper proposes a method based on spatial-spectral Schroedinger eigenmaps (SSSE) and multiple kernel learning (MKL) to classify hyperspectral images more efficiently while using a low number of training samples. In the proposed method, first SSSE is applied to spectral domain in order to extract significant features and reduce dimension of the original image. Then MKL is utilized to enhance the feature learning process and obtain an optimum combination of some specified kernels. Finally, the classification is carried out by substituting the optimal kernel in support vector machine (SVM) algorithm. Experimental results show that the proposed method improves classification accuracy significantly and provides highly efficient results in the case of a small number of training samples. Furthermore, the computation time of the proposed method is much lower than the state-of-the-art MKL methods.http://dx.doi.org/10.1080/07038992.2021.1978840 |
| spellingShingle | Shirin Hassanzadeh Habibollah Danyali Mohammad Sadegh Helfroush Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples Canadian Journal of Remote Sensing |
| title | Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples |
| title_full | Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples |
| title_fullStr | Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples |
| title_full_unstemmed | Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples |
| title_short | Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples |
| title_sort | combined spatial spectral schroedinger eigenmaps with multiple kernel learning for hyperspectral image classification using a low number of training samples |
| url | http://dx.doi.org/10.1080/07038992.2021.1978840 |
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