Polarimetric SAR Image Classification via Weighted Ensemble Based on 0-1 Matrix Decomposition

Chen Bo; Wang Shuang; Jiao Li-cheng; Liu Fang; Mao Sha-sha; Zhang Shuang

doi:10.11999/JEIT141059

Volume 37 Issue 6

Jun. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(6): 1495-1501

Chen Bo, Wang Shuang, Jiao Li-cheng, Liu Fang, Mao Sha-sha, Zhang Shuang. Polarimetric SAR Image Classification via Weighted Ensemble Based on 0-1 Matrix Decomposition[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1495-1501. doi: 10.11999/JEIT141059

Citation:

Chen Bo, Wang Shuang, Jiao Li-cheng, Liu Fang, Mao Sha-sha, Zhang Shuang. Polarimetric SAR Image Classification via Weighted Ensemble Based on 0-1 Matrix Decomposition[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1495-1501. doi: 10.11999/JEIT141059

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Polarimetric SAR Image Classification via Weighted Ensemble Based on 0-1 Matrix Decomposition

doi: 10.11999/JEIT141059

1.
(Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi&rsquo
2.
(Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi&rsquo

Received Date: 2014-08-11
Rev Recd Date: 2014-10-22
Publish Date: 2015-06-19

Abstract

Abstract

For Polarimetric SAR (PolSAR), because it contains more scattering information, thus it can provide more available features. How to use the features is crucial for the PolSAR image classification, however, there are no existing specific rules. To solve the above problem, a supervised Polarimetric SAR image classification method via weighted ensemble based on 0-1 matrix decomposition is proposed. The proposed method adopts matrix decomposition ensemble to learn on different feature subsets to get coefficients, and weighting ensemble algorithm is employed via the predictive results to improve the final classification results. Firstly, some features are extracted from PolSAR data as initial feature group and are divided randomly into several feature subsets. Then, according to the ensemble algorithm to get the different weights based on the feature subsets, small coefficients are assigned to bad classification results to decrease the harmful impact of some features. The final classification result is achieved by combining the results together. The experimental results of L-band and C-band PolSAR data demonstrate that the proposed method can effectively improve the classification results.
- Polarimetric SAR (PolSAR),
- Supervised image classification,
- Ensemble learning,
- Classifier ensemble

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References(24)

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