本文選題：極化SAR圖像分類 + 超完備字典�。� 參考：《信號處理》2017年11期

【摘要】：極化SAR圖像分類是一個高維非線性映射問題,稀疏表示(CS)對于解決此類問題具有很大潛力。字典學習在基于CS的分類中起到重要作用。本文提出了一種新的字典學習模型,用于增強字典的區(qū)分能力,使其更適合極化SAR圖像分類。提出的模型根據(jù)字典中兩類子字典在分類中的作用對其相應的表達系數(shù)施加不同的稀疏約束。為使共同子字典能夠抓住所有類共享的特征,對其相應系數(shù)施加稀疏約束,為使類專屬子字典能夠抓住類內獨享的局部和全局結構特征,對其相應系數(shù)同時施加稀疏和低秩約束。由于共同子字典表達所有類共享的特征,我們以測試樣本在類專屬子字典上的重建誤差作為準則進行分類。本文在AIRSAR的Flevoland數(shù)據(jù)集上對此算法進行驗證,實驗結果驗證了算法的有效性。
[Abstract]:Polarimetric SAR image classification is a high dimensional nonlinear mapping problem. Sparse representation (CSS) has great potential to solve this problem. Dictionary learning plays an important role in CS-based classification. In this paper, a new dictionary learning model is proposed, which is used to enhance the distinguishing ability of the dictionary and make it more suitable for polarimetric SAR image classification. The proposed model imposes different sparse constraints on the corresponding expression coefficients according to the role of two sub-dictionaries in the classification of dictionaries. In order to make the common sub-dictionary grasp the characteristics shared by all classes, and to impose sparse constraints on the corresponding coefficients, the class specific sub-dictionary can capture the local and global structural features that are unique to the class. Both sparse and low rank constraints are applied to the corresponding coefficients. Because the common sub-dictionary represents the characteristics shared by all classes, we use the error of the test sample reconstruction on the class specific sub-dictionary as the criterion for classification. The algorithm is validated on the Flevoland dataset of AIRSAR. The experimental results show that the algorithm is effective.
【作者單位】： 武漢大學電子信息學院;
【基金】：國家自然科學基金項目(61771014)
【分類號】：TN957.52
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本文編號：2040740