Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests
<p>Abstract</p> <p>Terrain classification using polarimetric SAR imagery has been a very active research field over recent years. Although lots of features have been proposed and many classifiers have been employed, there are few works on comparing these features and their combinat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2010-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://asp.eurasipjournals.com/content/2010/465612 |
| _version_ | 1818303719399227392 |
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| author | Zou Tongyuan Yang Wen Dai Dengxin Sun Hong |
| author_facet | Zou Tongyuan Yang Wen Dai Dengxin Sun Hong |
| author_sort | Zou Tongyuan |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Terrain classification using polarimetric SAR imagery has been a very active research field over recent years. Although lots of features have been proposed and many classifiers have been employed, there are few works on comparing these features and their combination with different classifiers. In this paper, we firstly evaluate and compare different features for classifying polarimetric SAR imagery. Then, we propose two strategies for feature combination: manual selection according to heuristic rules and automatic combination based on a simple but efficient criterion. Finally, we introduce extremely randomized clustering forests (ERCFs) to polarimetric SAR image classification and compare it with other competitive classifiers. Experiments on ALOS PALSAR image validate the effectiveness of the feature combination strategies and also show that ERCFs achieves competitive performance with other widely used classifiers while costing much less training and testing time.</p> |
| first_indexed | 2024-12-13T05:59:16Z |
| format | Article |
| id | doaj.art-12c6c84144dc483ba7d093275ac4c6a0 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-13T05:59:16Z |
| publishDate | 2010-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-12c6c84144dc483ba7d093275ac4c6a02022-12-21T23:57:22ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-0120101465612Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering ForestsZou TongyuanYang WenDai DengxinSun Hong<p>Abstract</p> <p>Terrain classification using polarimetric SAR imagery has been a very active research field over recent years. Although lots of features have been proposed and many classifiers have been employed, there are few works on comparing these features and their combination with different classifiers. In this paper, we firstly evaluate and compare different features for classifying polarimetric SAR imagery. Then, we propose two strategies for feature combination: manual selection according to heuristic rules and automatic combination based on a simple but efficient criterion. Finally, we introduce extremely randomized clustering forests (ERCFs) to polarimetric SAR image classification and compare it with other competitive classifiers. Experiments on ALOS PALSAR image validate the effectiveness of the feature combination strategies and also show that ERCFs achieves competitive performance with other widely used classifiers while costing much less training and testing time.</p>http://asp.eurasipjournals.com/content/2010/465612 |
| spellingShingle | Zou Tongyuan Yang Wen Dai Dengxin Sun Hong Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests EURASIP Journal on Advances in Signal Processing |
| title | Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests |
| title_full | Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests |
| title_fullStr | Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests |
| title_full_unstemmed | Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests |
| title_short | Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests |
| title_sort | polarimetric sar image classification using multifeatures combination and extremely randomized clustering forests |
| url | http://asp.eurasipjournals.com/content/2010/465612 |
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