Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model
Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-09-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/8/9/748 |
| _version_ | 1819001250974269440 |
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| author | Yi Wang Haiwei Song Yan Zhang |
| author_facet | Yi Wang Haiwei Song Yan Zhang |
| author_sort | Yi Wang |
| collection | DOAJ |
| description | Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In this method, a novel technique for labeling regions obtained by the spectral-spatial segmentation process is presented. Our method includes the following steps. First, the probabilistic support vector machines (SVM) classifier is used to estimate probabilities belonging to each information class. Second, an extended JBF is employed to perform image smoothing on the probability maps. By using our JBF process, salt-and-pepper classification noise in homogeneous regions can be effectively smoothed out while object boundaries in the original image are better preserved as well. Third, a sequence of modified bi-labeling graph cut models is constructed for each information class to extract the desirable object belonging to the corresponding class from the smoothed probability maps. Finally, a classification map is achieved by merging the segmentation maps obtained in the last step using a simple and effective rule. Experimental results based on three benchmark airborne hyperspectral datasets with different resolutions and contexts demonstrate that our method can achieve 8.56%–13.68% higher overall accuracies than the pixel-wise SVM classifier. The performance of our method was further compared to several classical hyperspectral image classification methods using objective quantitative measures and a visual qualitative evaluation. |
| first_indexed | 2024-12-20T22:46:14Z |
| format | Article |
| id | doaj.art-a1ec7e9090ca48eb9ae2b78597668d4d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T22:46:14Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a1ec7e9090ca48eb9ae2b78597668d4d2022-12-21T19:24:23ZengMDPI AGRemote Sensing2072-42922016-09-018974810.3390/rs8090748rs8090748Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based ModelYi Wang0Haiwei Song1Yan Zhang2Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaInstitute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaInstitute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaHyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In this method, a novel technique for labeling regions obtained by the spectral-spatial segmentation process is presented. Our method includes the following steps. First, the probabilistic support vector machines (SVM) classifier is used to estimate probabilities belonging to each information class. Second, an extended JBF is employed to perform image smoothing on the probability maps. By using our JBF process, salt-and-pepper classification noise in homogeneous regions can be effectively smoothed out while object boundaries in the original image are better preserved as well. Third, a sequence of modified bi-labeling graph cut models is constructed for each information class to extract the desirable object belonging to the corresponding class from the smoothed probability maps. Finally, a classification map is achieved by merging the segmentation maps obtained in the last step using a simple and effective rule. Experimental results based on three benchmark airborne hyperspectral datasets with different resolutions and contexts demonstrate that our method can achieve 8.56%–13.68% higher overall accuracies than the pixel-wise SVM classifier. The performance of our method was further compared to several classical hyperspectral image classification methods using objective quantitative measures and a visual qualitative evaluation.http://www.mdpi.com/2072-4292/8/9/748hyperspectral imagesclassificationspectral-spatialgraph cutjoint bilateral filter |
| spellingShingle | Yi Wang Haiwei Song Yan Zhang Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model Remote Sensing hyperspectral images classification spectral-spatial graph cut joint bilateral filter |
| title | Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model |
| title_full | Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model |
| title_fullStr | Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model |
| title_full_unstemmed | Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model |
| title_short | Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model |
| title_sort | spectral spatial classification of hyperspectral images using joint bilateral filter and graph cut based model |
| topic | hyperspectral images classification spectral-spatial graph cut joint bilateral filter |
| url | http://www.mdpi.com/2072-4292/8/9/748 |
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