Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing
Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability’s influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of endmembers and fully constrained least squares bas...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2022.2125447 |
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| author | Qiang Guan Tongyu Xu Shuai Feng Fenghua Yu Kai Song |
| author_facet | Qiang Guan Tongyu Xu Shuai Feng Fenghua Yu Kai Song |
| author_sort | Qiang Guan |
| collection | DOAJ |
| description | Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability’s influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of endmembers and fully constrained least squares based on distance strategy (D-FCLS) to improve the efficiency of abundance estimation. In the segmentation-scale determination method, this study establishes a segmentation scale division criterion to divide segmented images with similar quality into the same segmentation scale. The optimal segmentation scale is selected according to the actual situation of hyperspectral images. Moreover, the distance strategy is applied to fully constrained least squares (FCLS) using the spatial relationship between endmembers and the mixed pixel in abundance estimation. The proposed methods are evaluated on the synthetic and real datasets. The results show that the validity of the segmentation-scale determination method is verified by quantitative and qualitative evaluation on all datasets. In terms of abundance estimation, compared with FCLS, D-FCLS improves the efficiency by more than 10.30% on the synthetic dataset and 18.71% on the real dataset. In addition, this study’s proposed abundance estimation method and unsupervised superpixel-based HU method are superior to the other comparison methods. |
| format | Article |
| id | doaj-art-a64943469e0c45f69740fdeaf3f6492c |
| institution | OA Journals |
| issn | 2279-7254 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-a64943469e0c45f69740fdeaf3f6492c2025-08-20T02:38:11ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542022-12-0155148550610.1080/22797254.2022.2125447Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral UnmixingQiang Guan0Tongyu Xu1Shuai Feng2Fenghua Yu3Kai Song4College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, Liaoning, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, Liaoning, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, Liaoning, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, Liaoning, ChinaSchool of Information Science and Engineering, Shenyang Ligong University, Shenyang, Liaoning, ChinaSuperpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability’s influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of endmembers and fully constrained least squares based on distance strategy (D-FCLS) to improve the efficiency of abundance estimation. In the segmentation-scale determination method, this study establishes a segmentation scale division criterion to divide segmented images with similar quality into the same segmentation scale. The optimal segmentation scale is selected according to the actual situation of hyperspectral images. Moreover, the distance strategy is applied to fully constrained least squares (FCLS) using the spatial relationship between endmembers and the mixed pixel in abundance estimation. The proposed methods are evaluated on the synthetic and real datasets. The results show that the validity of the segmentation-scale determination method is verified by quantitative and qualitative evaluation on all datasets. In terms of abundance estimation, compared with FCLS, D-FCLS improves the efficiency by more than 10.30% on the synthetic dataset and 18.71% on the real dataset. In addition, this study’s proposed abundance estimation method and unsupervised superpixel-based HU method are superior to the other comparison methods.https://www.tandfonline.com/doi/10.1080/22797254.2022.2125447Hyperspectral unmixingsuperpixel segmentationsegmentation scaleabundance estimationfully constrained least squaresdistance strategy |
| spellingShingle | Qiang Guan Tongyu Xu Shuai Feng Fenghua Yu Kai Song Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing European Journal of Remote Sensing Hyperspectral unmixing superpixel segmentation segmentation scale abundance estimation fully constrained least squares distance strategy |
| title | Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing |
| title_full | Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing |
| title_fullStr | Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing |
| title_full_unstemmed | Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing |
| title_short | Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing |
| title_sort | optimal segmentation and improved abundance estimation for superpixel based hyperspectral unmixing |
| topic | Hyperspectral unmixing superpixel segmentation segmentation scale abundance estimation fully constrained least squares distance strategy |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2022.2125447 |
| work_keys_str_mv | AT qiangguan optimalsegmentationandimprovedabundanceestimationforsuperpixelbasedhyperspectralunmixing AT tongyuxu optimalsegmentationandimprovedabundanceestimationforsuperpixelbasedhyperspectralunmixing AT shuaifeng optimalsegmentationandimprovedabundanceestimationforsuperpixelbasedhyperspectralunmixing AT fenghuayu optimalsegmentationandimprovedabundanceestimationforsuperpixelbasedhyperspectralunmixing AT kaisong optimalsegmentationandimprovedabundanceestimationforsuperpixelbasedhyperspectralunmixing |