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SAR Image Classification via Hierarchical Sparse Representation and Multisize Patch Features

机译：通过分层稀疏表示和多尺寸补丁特征进行SAR图像分类

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摘要

In this letter, a novel hierarchical sparse representation-based classification (HSRC) for synthetic aperture radar (SAR) images is proposed. Features utilized in HSRC are extracted from the multisize patches around each pixel to precisely describe the complex terrains. Two thresholds are introduced in the sparse representation classifier to restrict the range of reconstruction residual, which classifies the reliable classified points, and the rest of the pixels are considered as the uncertain ones in the original SAR image. Then, a new dictionary is constructed by the reliable pixels, and the uncertain pixels will be reclassified in the next classification layer. The hierarchical structure is very reasonable and effective to employ simple features in each layer for describing the various topographic types. Compared with traditional sparse representation-based classification and support vector machines in several fixed-size patches, the proposed method can obtain better performance both in quantitative evaluation and visualization results.
机译：在这封信中，提出了一种用于合成孔径雷达（SAR）图像的新型基于分层稀疏表示的分类（HSRC）。 HSRC中使用的特征是从每个像素周围的多尺寸补丁中提取的，以精确描述复杂的地形。在稀疏表示分类器中引入两个阈值来限制重建残差的范围，从而对可靠的分类点进行分类，其余像素被视为原始SAR图像中的不确定像素。然后，由可靠像素构建新字典，不确定像素将在下一个分类层中重新分类。分层结构非常合理且有效，可以在每一层中使用简单的功能来描述各种地形类型。与传统的基于稀疏表示的分类和支持向量机在几个固定大小的补丁中相比，该方法在定量评估和可视化结果上均具有更好的性能。

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  • 来源
    《IEEE Geoscience and Remote Sensing Letters》 |2016年第1期|33-37|共5页
  • 作者

    Biao Hou; Bo Ren; Guilin Ju; Huiyan Li; Licheng Jiao; Jin Zhao;

  • 作者单位

    Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi'an, China;

    Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi'an, China;

    Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi'an, China;

    Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi'an, China;

    Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi'an, China;

    Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi'an, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Synthetic aperture radar; Support vector machines; Training; Feature extraction; Remote sensing;

    机译：合成孔径雷达;支持向量机;训练;特征提取;遥感;

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