Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division
The traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects’ scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2018/3918954 |
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| _version_ | 1850167322592935936 |
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| author | Wen Zhou Dongping Ming Lu Xu Hanqing Bao Min Wang |
| author_facet | Wen Zhou Dongping Ming Lu Xu Hanqing Bao Min Wang |
| author_sort | Wen Zhou |
| collection | DOAJ |
| description | The traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects’ scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene and form a scene. Based on this scenario, this paper proposes a stratified object-oriented image analysis method based on remote sensing image scene division. This method firstly uses middle semantic which can reflect an image’s visual complexity to classify the remote sensing image into different scenes, and then within each scene, an improved grid search algorithm is employed to optimize the segmentation result of each scene, so that the optimal scale can be utmostly adopted for each scene. Because the complexity of data is effectively reduced by stratified processing, local scale optimization ensures the overall classification accuracy of the whole image, which is practically meaningful for remote sensing geo-application. |
| format | Article |
| id | doaj-art-e202386c58234e259968d22fcf868180 |
| institution | OA Journals |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-e202386c58234e259968d22fcf8681802025-08-20T02:21:13ZengWileyJournal of Spectroscopy2314-49202314-49392018-01-01201810.1155/2018/39189543918954Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene DivisionWen Zhou0Dongping Ming1Lu Xu2Hanqing Bao3Min Wang4School of Information Engineering, China University of Geosciences, 29 Xueyuan Road, Haidian, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, 29 Xueyuan Road, Haidian, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, 29 Xueyuan Road, Haidian, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, 29 Xueyuan Road, Haidian, Beijing 100083, ChinaKey Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, Jiangsu, ChinaThe traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects’ scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene and form a scene. Based on this scenario, this paper proposes a stratified object-oriented image analysis method based on remote sensing image scene division. This method firstly uses middle semantic which can reflect an image’s visual complexity to classify the remote sensing image into different scenes, and then within each scene, an improved grid search algorithm is employed to optimize the segmentation result of each scene, so that the optimal scale can be utmostly adopted for each scene. Because the complexity of data is effectively reduced by stratified processing, local scale optimization ensures the overall classification accuracy of the whole image, which is practically meaningful for remote sensing geo-application.http://dx.doi.org/10.1155/2018/3918954 |
| spellingShingle | Wen Zhou Dongping Ming Lu Xu Hanqing Bao Min Wang Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division Journal of Spectroscopy |
| title | Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division |
| title_full | Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division |
| title_fullStr | Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division |
| title_full_unstemmed | Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division |
| title_short | Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division |
| title_sort | stratified object oriented image classification based on remote sensing image scene division |
| url | http://dx.doi.org/10.1155/2018/3918954 |
| work_keys_str_mv | AT wenzhou stratifiedobjectorientedimageclassificationbasedonremotesensingimagescenedivision AT dongpingming stratifiedobjectorientedimageclassificationbasedonremotesensingimagescenedivision AT luxu stratifiedobjectorientedimageclassificationbasedonremotesensingimagescenedivision AT hanqingbao stratifiedobjectorientedimageclassificationbasedonremotesensingimagescenedivision AT minwang stratifiedobjectorientedimageclassificationbasedonremotesensingimagescenedivision |