Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement
Popular unsupervised change detection algorithms suffer from two problems: first, the difference image generated by bitemporal images usually includes a large number of falsely changed regions due to noise corruption and illumination change; second, fuzzy clustering algorithms are sensitive to noise...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9305230/ |
| _version_ | 1818821135428485120 |
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| author | Dinghua Xue Tao Lei Xiaohong Jia Xingwu Wang Tao Chen Asoke K. Nandi |
| author_facet | Dinghua Xue Tao Lei Xiaohong Jia Xingwu Wang Tao Chen Asoke K. Nandi |
| author_sort | Dinghua Xue |
| collection | DOAJ |
| description | Popular unsupervised change detection algorithms suffer from two problems: first, the difference image generated by bitemporal images usually includes a large number of falsely changed regions due to noise corruption and illumination change; second, fuzzy clustering algorithms are sensitive to noise and they miss the relationship among feature components. To address these issues, we propose a multiscale and multiresolution Gaussian-mixture-model guided by saliency-enhancement (SE-MGMM) for change detection in bitemporal remote sensing images. The proposed SE-MGMM makes two contributions. The first is a novel salient strategy that can enhance saliency objects while suppressing the image background. The strategy uses the saliency weight information to enhance changed regions leading to the improvement of grayscale contrast between changed regions and unchanged regions. The second is that we present a Gaussian-mixture-model based on spatial multiscale and frequency multiresolution information fusion, which can effectively utilize features of difference images and improve detection results of changed regions. Experiments show that the proposed SE-MGMM is robust for both very high-resolution remote sensing images and synthetic aperture radar images. Moreover, the SE-MGMM achieves better change detection and provides better performance metrics than state-of-the-art approaches. |
| first_indexed | 2024-12-18T23:03:22Z |
| format | Article |
| id | doaj.art-e31ed5b0750d40c2a394d04440d39ae4 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-18T23:03:22Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-e31ed5b0750d40c2a394d04440d39ae42022-12-21T20:48:32ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01141796180910.1109/JSTARS.2020.30468389305230Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency EnhancementDinghua Xue0Tao Lei1https://orcid.org/0000-0002-2104-9298Xiaohong Jia2https://orcid.org/0000-0002-4853-4779Xingwu Wang3Tao Chen4https://orcid.org/0000-0001-6965-1256Asoke K. Nandi5https://orcid.org/0000-0001-6248-2875School of Electronical and Control Engineering and the Shaanxi Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, ChinaSchool of Electronic Information and Artificial Intelligence and the Shaanxi Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, ChinaSchool of Electronical and Control Engineering and the Shaanxi Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, ChinaSchool of Electronic Information and Artifical Intelligence, Shaanxi University of Science and Technology, Xi’an, ChinaInstitute of Geophysics and Geomatics, China University of Geosciences, Wuhan, ChinaDepartment of Electronic and Electrical Engineering, Brunel University London, Uxbridge, U.K.Popular unsupervised change detection algorithms suffer from two problems: first, the difference image generated by bitemporal images usually includes a large number of falsely changed regions due to noise corruption and illumination change; second, fuzzy clustering algorithms are sensitive to noise and they miss the relationship among feature components. To address these issues, we propose a multiscale and multiresolution Gaussian-mixture-model guided by saliency-enhancement (SE-MGMM) for change detection in bitemporal remote sensing images. The proposed SE-MGMM makes two contributions. The first is a novel salient strategy that can enhance saliency objects while suppressing the image background. The strategy uses the saliency weight information to enhance changed regions leading to the improvement of grayscale contrast between changed regions and unchanged regions. The second is that we present a Gaussian-mixture-model based on spatial multiscale and frequency multiresolution information fusion, which can effectively utilize features of difference images and improve detection results of changed regions. Experiments show that the proposed SE-MGMM is robust for both very high-resolution remote sensing images and synthetic aperture radar images. Moreover, the SE-MGMM achieves better change detection and provides better performance metrics than state-of-the-art approaches.https://ieeexplore.ieee.org/document/9305230/Change detectionfeature fusionGaussian-mixture-model (GMM)saliency enhancement |
| spellingShingle | Dinghua Xue Tao Lei Xiaohong Jia Xingwu Wang Tao Chen Asoke K. Nandi Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change detection feature fusion Gaussian-mixture-model (GMM) saliency enhancement |
| title | Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement |
| title_full | Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement |
| title_fullStr | Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement |
| title_full_unstemmed | Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement |
| title_short | Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement |
| title_sort | unsupervised change detection using multiscale and multiresolution gaussian mixture model guided by saliency enhancement |
| topic | Change detection feature fusion Gaussian-mixture-model (GMM) saliency enhancement |
| url | https://ieeexplore.ieee.org/document/9305230/ |
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