Automatic Change Detection of High Resolution Remote Sensing Images Based on Level Set Evolution and Support Vector Machine Classification
We propose a method for change detection in highresolution remote sensing images by means of level set evolution and Support Vector Machine (SVM) classification, which combined both pixellevel method and objectlevel method Both pixelbased change features and objectbased ones are extracted to improve...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2019-02-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1640 |
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| _version_ | 1849233790921080832 |
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| author | YAN Ming CAO Guo XIA Meng |
| author_facet | YAN Ming CAO Guo XIA Meng |
| author_sort | YAN Ming |
| collection | DOAJ |
| description | We propose a method for change detection in highresolution remote sensing images by means of level set evolution and Support Vector Machine (SVM) classification, which combined both pixellevel method and objectlevel method Both pixelbased change features and objectbased ones are extracted to improve the discriminability between the changed class and the unchanged classAt the pixellevel, the change detection problem is formulated as a segmentation issue using level set evolution in the difference image At the objectlevel, potential training samples are selectedfrom the segmentation results without manual intervention into SVM classifier Thereafter, the final changes are obtained by combining the pixelbased changes and the objectbased changes A chief advantage of our approach is being able to select appropriate samples for SVM classifier training Furthermore, our proposed method helps improving the accuracy and the degree of automation We systematically evaluated it with a variety of SPOT5 images and aerial images Experimental results demonstrated the accuracy of our proposed method |
| format | Article |
| id | doaj-art-8a38191d4d514bf4ae92a5ad6056aa93 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2019-02-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-8a38191d4d514bf4ae92a5ad6056aa932025-08-20T04:03:25ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832019-02-012401788410.15938/j.jhust.2019.01.013Automatic Change Detection of High Resolution Remote Sensing Images Based on Level Set Evolution and Support Vector Machine ClassificationYAN Ming0CAO Guo1XIA Meng2School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, ChinaWe propose a method for change detection in highresolution remote sensing images by means of level set evolution and Support Vector Machine (SVM) classification, which combined both pixellevel method and objectlevel method Both pixelbased change features and objectbased ones are extracted to improve the discriminability between the changed class and the unchanged classAt the pixellevel, the change detection problem is formulated as a segmentation issue using level set evolution in the difference image At the objectlevel, potential training samples are selectedfrom the segmentation results without manual intervention into SVM classifier Thereafter, the final changes are obtained by combining the pixelbased changes and the objectbased changes A chief advantage of our approach is being able to select appropriate samples for SVM classifier training Furthermore, our proposed method helps improving the accuracy and the degree of automation We systematically evaluated it with a variety of SPOT5 images and aerial images Experimental results demonstrated the accuracy of our proposed methodhttps://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1640change detectionlevel set evolutionsupport vector machine (svm)multi resolution analysisimage segmentation |
| spellingShingle | YAN Ming CAO Guo XIA Meng Automatic Change Detection of High Resolution Remote Sensing Images Based on Level Set Evolution and Support Vector Machine Classification Journal of Harbin University of Science and Technology change detection level set evolution support vector machine (svm) multi resolution analysis image segmentation |
| title | Automatic Change Detection of High Resolution Remote Sensing Images Based on Level Set Evolution and Support Vector Machine Classification |
| title_full | Automatic Change Detection of High Resolution Remote Sensing Images Based on Level Set Evolution and Support Vector Machine Classification |
| title_fullStr | Automatic Change Detection of High Resolution Remote Sensing Images Based on Level Set Evolution and Support Vector Machine Classification |
| title_full_unstemmed | Automatic Change Detection of High Resolution Remote Sensing Images Based on Level Set Evolution and Support Vector Machine Classification |
| title_short | Automatic Change Detection of High Resolution Remote Sensing Images Based on Level Set Evolution and Support Vector Machine Classification |
| title_sort | automatic change detection of high resolution remote sensing images based on level set evolution and support vector machine classification |
| topic | change detection level set evolution support vector machine (svm) multi resolution analysis image segmentation |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1640 |
| work_keys_str_mv | AT yanming automaticchangedetectionofhighresolutionremotesensingimagesbasedonlevelsetevolutionandsupportvectormachineclassification AT caoguo automaticchangedetectionofhighresolutionremotesensingimagesbasedonlevelsetevolutionandsupportvectormachineclassification AT xiameng automaticchangedetectionofhighresolutionremotesensingimagesbasedonlevelsetevolutionandsupportvectormachineclassification |