A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest
The purposes of the algorithm presented in this paper are to select features with the highest average separability by using the random forest method to distinguish categories that are easy to distinguish and to select the most divisible features from the most difficult categories using the weighted...
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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: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2018/3521720 |
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| author | Mengmeng Sun Chunyang Wang Shuangting Wang Zongze Zhao Xiao Li |
| author_facet | Mengmeng Sun Chunyang Wang Shuangting Wang Zongze Zhao Xiao Li |
| author_sort | Mengmeng Sun |
| collection | DOAJ |
| description | The purposes of the algorithm presented in this paper are to select features with the highest average separability by using the random forest method to distinguish categories that are easy to distinguish and to select the most divisible features from the most difficult categories using the weighted entropy algorithm. The framework is composed of five parts: (1) random samples selection with (2) probabilistic output initial random forest classification processing based on the number of votes; (3) semisupervised classification, which is an improvement of the supervision classification of random forest based on the weighted entropy algorithm; (4) precision evaluation; and (5) a comparison with the traditional minimum distance classification and the support vector machine (SVM) classification. In order to verify the universality of the proposed algorithm, two different data sources are tested, which are AVIRIS and Hyperion data. The results show that the overall classification accuracy of AVIRIS data is up to 87.36%, the kappa coefficient is up to 0.8591, and the classification time is 22.72s. Hyperion data is up to 99.17%, the kappa coefficient is up to 0.9904, and the classification time is 8.16s. Classification accuracy is obviously improved and efficiency is greatly improved, compared with the minimum distance and the SVM classifier and the CART classifier. |
| format | Article |
| id | doaj-art-75a6d1c02c044265a4b695f995b0fe8a |
| institution | OA Journals |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-75a6d1c02c044265a4b695f995b0fe8a2025-08-20T02:07:01ZengWileyAdvances in Multimedia1687-56801687-56992018-01-01201810.1155/2018/35217203521720A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random ForestMengmeng Sun0Chunyang Wang1Shuangting Wang2Zongze Zhao3Xiao Li4School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaThe purposes of the algorithm presented in this paper are to select features with the highest average separability by using the random forest method to distinguish categories that are easy to distinguish and to select the most divisible features from the most difficult categories using the weighted entropy algorithm. The framework is composed of five parts: (1) random samples selection with (2) probabilistic output initial random forest classification processing based on the number of votes; (3) semisupervised classification, which is an improvement of the supervision classification of random forest based on the weighted entropy algorithm; (4) precision evaluation; and (5) a comparison with the traditional minimum distance classification and the support vector machine (SVM) classification. In order to verify the universality of the proposed algorithm, two different data sources are tested, which are AVIRIS and Hyperion data. The results show that the overall classification accuracy of AVIRIS data is up to 87.36%, the kappa coefficient is up to 0.8591, and the classification time is 22.72s. Hyperion data is up to 99.17%, the kappa coefficient is up to 0.9904, and the classification time is 8.16s. Classification accuracy is obviously improved and efficiency is greatly improved, compared with the minimum distance and the SVM classifier and the CART classifier.http://dx.doi.org/10.1155/2018/3521720 |
| spellingShingle | Mengmeng Sun Chunyang Wang Shuangting Wang Zongze Zhao Xiao Li A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest Advances in Multimedia |
| title | A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest |
| title_full | A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest |
| title_fullStr | A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest |
| title_full_unstemmed | A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest |
| title_short | A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest |
| title_sort | new semisupervised entropy framework of hyperspectral image classification based on random forest |
| url | http://dx.doi.org/10.1155/2018/3521720 |
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