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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Bag-of-Visual-Words Based on Clonal Selection Algorithm for SAR Image Classification
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Bag-of-Visual-Words Based on Clonal Selection Algorithm for SAR Image Classification

机译：基于克隆选择算法的视觉词袋SAR图像分类

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摘要

Synthetic aperture radar (SAR) image classification involves two crucial issues: suitable feature representation technique and effective pattern classification methodology. Here, we concentrate on the first issue. By exploiting a famous image feature processing strategy, Bag-of-Visual-Words (BOV) in image semantic analysis and the artificial immune systems (AIS)'s abilities of learning and adaptability to solve complicated problems, we present a novel and effective image representation method for SAR image classification. In BOV, an effective fused feature sets for local feature representation are first formulated, which are viewed as the low-level features in it. After that, clonal selection algorithm (CSA) in AIS is introduced to optimize the prediction error of k-fold cross-validation for getting more suitable visual words from the low-level features. Finally, the BOV features are represented by the learned visual words for subsequent pattern classification. Compared with the other four algorithms, the proposed algorithm obtains more satisfactory and cogent classification experimental results.
机译：合成孔径雷达（SAR）图像分类涉及两个关键问题：合适的特征表示技术和有效的模式分类方法。在这里，我们专注于第一个问题。通过利用著名的图像特征处理策略，图像语义分析中的视觉词袋（BOV）以及人工免疫系统（AIS）的学习能力和解决复杂问题的适应性，我们提出了一种新颖而有效的图像SAR图像分类的一种表示方法。在BOV中，首先制定了用于局部特征表示的有效融合特征集，这些特征集被视为其中的低级特征。此后，引入AIS中的克隆选择算法（CSA）来优化k倍交叉验证的预测误差，以便从低级特征中获得更合适的视觉单词。最后，BOV特征由所学的视觉词表示，用于后续的模式分类。与其他四种算法相比，该算法获得了更令人满意和切实可行的分类实验结果。

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