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利用0-1矩陣分解集成的極化SAR圖像分類

陳博,  王爽,  焦李成,  劉芳,  毛莎莎,  張爽

陳博, 王爽, 焦李成, 劉芳, 毛莎莎, 張爽. 利用0-1矩陣分解集成的極化SAR圖像分類[J]. 電子與信息學(xué)報, 2015, 37(6): 1495-1501. doi: 10.11999/JEIT141059
引用本文: 陳博, 王爽, 焦李成, 劉芳, 毛莎莎, 張爽. 利用0-1矩陣分解集成的極化SAR圖像分類[J]. 電子與信息學(xué)報, 2015, 37(6): 1495-1501. doi: 10.11999/JEIT141059
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

利用0-1矩陣分解集成的極化SAR圖像分類

doi: 10.11999/JEIT141059
  • 2.

    (西安電子科技大學(xué)智能感知與圖像理解教育部重點實驗室 西安 710071)

基金項目: 

國家973計劃項目(2013CB329402)，國家自然科學(xué)基金(61271302, 61272282, 61202176, 61271298)和國家教育部博士點基金(20100203120005)資助課題

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

  • 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

  • 摘要: 全極化合成孔徑雷達(PolSAR)圖像蘊含更豐富的散射信息，具有更多的可用特征。如何使用這些特征是極化SAR圖像分類中非常重要的一步，但是目前尚未對此提出非常明確的準(zhǔn)則。為了能夠有效地解決上述問題，該文提出一種基于特征加權(quán)集成的極化SAR圖像分類算法。該算法采用0-1矩陣分解集成方法對包括不同特征的數(shù)據(jù)集進行學(xué)習(xí)獲得相應(yīng)加權(quán)系數(shù)，并通過對每個特征集獲得的預(yù)測結(jié)果進行加權(quán)集成來提高極化SAR圖像分類性能。首先，輸入極化SAR數(shù)據(jù)，獲得極化特征作為原始特征集，并對其進行隨機抽取獲得不同的特征子集；然后，使用0-1矩陣集成算法得到每個特征值相對應(yīng)的加權(quán)系數(shù)；最后，通過對各個特征子集的預(yù)測結(jié)果進行集成得到最終極化SAR圖像分類結(jié)果。實測L波段和C波段極化數(shù)據(jù)的實驗結(jié)果表明，該算法可以有效地提高極化SAR圖像分類的準(zhǔn)確度。
    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.
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出版歷程
  • 收稿日期:  2014-08-11
  • 修回日期:  2014-10-22
  • 刊出日期:  2015-06-19

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