Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area
A novel approach is proposed for classifying the polarimetric SAR (PolSAR) data by integrating polarimetric decomposition, sub-aperture decomposition and decision tree algorithm. It is composed of three key steps: sub-aperture decomposition, feature extraction and combination, and decision tree clas...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-01-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/7/2/1380 |
| _version_ | 1819110813770711040 |
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| author | Lei Deng Ya-nan Yan Chen Sun |
| author_facet | Lei Deng Ya-nan Yan Chen Sun |
| author_sort | Lei Deng |
| collection | DOAJ |
| description | A novel approach is proposed for classifying the polarimetric SAR (PolSAR) data by integrating polarimetric decomposition, sub-aperture decomposition and decision tree algorithm. It is composed of three key steps: sub-aperture decomposition, feature extraction and combination, and decision tree classification. Feature extraction and combination is the main contribution to the innovation of the proposed method. Firstly, the full-resolution PolSAR image and its two sub-aperture images are decomposed to obtain the scattering entropy, average scattering angle and anisotropy, respectively. Then, the difference information between the two sub-aperture images are extracted, and combined with the target decomposition features from full-resolution images to form the classification feature set. Finally, C5.0 decision tree algorithm is used to classify the PolSAR image. A comparison between the proposed method and commonly-used Wishart supervised classification was made to verify the improvement of the proposed method on the classification. The overall accuracy using the proposed method was 88.39%, much higher than that using the Wishart supervised classification, which exhibited an overall accuracy of 69.82%. The Kappa Coefficient was 0.83, whereas that using the Wishart supervised classification was 0.56. The results indicate that the proposed method performed better than Wishart supervised classification for landscape classification in urban area using PolSAR data. Further investigation was carried out on the contribution of difference information to PolSAR classification. It was found that the sub-aperture decomposition improved the classification accuracy of forest, buildings and grassland effectively in high-density urban area. Compared with support vector machine (SVM) and QUEST classifier, C5.0 decision tree classifier performs more efficient in time consumption, feature selection and construction of decision rule. |
| first_indexed | 2024-12-22T03:47:41Z |
| format | Article |
| id | doaj.art-6712bdab275041778f7a57e11bff893a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-22T03:47:41Z |
| publishDate | 2015-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6712bdab275041778f7a57e11bff893a2022-12-21T18:40:05ZengMDPI AGRemote Sensing2072-42922015-01-01721380139610.3390/rs70201380rs70201380Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban AreaLei Deng0Ya-nan Yan1Chen Sun2College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resource Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resource Environment and Tourism, Capital Normal University, Beijing 100048, ChinaA novel approach is proposed for classifying the polarimetric SAR (PolSAR) data by integrating polarimetric decomposition, sub-aperture decomposition and decision tree algorithm. It is composed of three key steps: sub-aperture decomposition, feature extraction and combination, and decision tree classification. Feature extraction and combination is the main contribution to the innovation of the proposed method. Firstly, the full-resolution PolSAR image and its two sub-aperture images are decomposed to obtain the scattering entropy, average scattering angle and anisotropy, respectively. Then, the difference information between the two sub-aperture images are extracted, and combined with the target decomposition features from full-resolution images to form the classification feature set. Finally, C5.0 decision tree algorithm is used to classify the PolSAR image. A comparison between the proposed method and commonly-used Wishart supervised classification was made to verify the improvement of the proposed method on the classification. The overall accuracy using the proposed method was 88.39%, much higher than that using the Wishart supervised classification, which exhibited an overall accuracy of 69.82%. The Kappa Coefficient was 0.83, whereas that using the Wishart supervised classification was 0.56. The results indicate that the proposed method performed better than Wishart supervised classification for landscape classification in urban area using PolSAR data. Further investigation was carried out on the contribution of difference information to PolSAR classification. It was found that the sub-aperture decomposition improved the classification accuracy of forest, buildings and grassland effectively in high-density urban area. Compared with support vector machine (SVM) and QUEST classifier, C5.0 decision tree classifier performs more efficient in time consumption, feature selection and construction of decision rule.http://www.mdpi.com/2072-4292/7/2/1380polarimetric SARsub-aperture decompositionpolarimetric decompositiondecision tree |
| spellingShingle | Lei Deng Ya-nan Yan Chen Sun Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area Remote Sensing polarimetric SAR sub-aperture decomposition polarimetric decomposition decision tree |
| title | Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area |
| title_full | Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area |
| title_fullStr | Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area |
| title_full_unstemmed | Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area |
| title_short | Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area |
| title_sort | use of sub aperture decomposition for supervised polsar classification in urban area |
| topic | polarimetric SAR sub-aperture decomposition polarimetric decomposition decision tree |
| url | http://www.mdpi.com/2072-4292/7/2/1380 |
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