SAR target classification using sparse representations and spatial pyramids
SAR target classification using sparse representations and spatial pyramids
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DOI：
10.1109/radar.2011.5960546
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发表时间：
2011-05
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影响因子：
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通讯作者：
P. Knee;Jayaraman J. Thiagarajan;K. Ramamurthy;A. Spanias
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文献类型：
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作者：
P. Knee;Jayaraman J. Thiagarajan;K. Ramamurthy;A. Spanias
我们考虑的问题，自动分类目标的合成孔径雷达（SAR）图像使用图像分割和稀疏表示的特征向量生成。具体来说，我们扩展了空间金字塔的方法，在该方法中，图像被分割成越来越精细的子区域，通过使用稀疏表示来描述每个子区域中的局部特征。这些特征描述符是通过识别那些字典元素来生成的，这些字典元素是通过k均值聚类创建的，它们最接近每个子区域的局部特征。通过系统地组合每个金字塔级别的结果，近似几何匹配有助于分类能力。使用线性SVM与SIFT、FFT幅度和基于DCT的局部特征描述符一起沿着进行分类的结果表明，使用来自字典的单个元素来描述局部特征足以进行准确的目标分类。将讨论在特征提取和分类方面的持续工作，重点放在重目标遮挡中分类的需要上。
We consider the problem of automatically classifying targets in synthetic aperture radar (SAR) imagery using image partitioning and sparse representation based feature vector generation. Specifically, we extend the spatial pyramid approach, in which the image is partitioned into increasingly fine sub-regions, by using a sparse representation to describe the local features in each sub-region. These feature descriptors are generated by identifying those dictionary elements, created via k-means clustering, that best approximate the local features for each sub-region. By systematically combining the results at each pyramid level, classification ability is facilitated by approximate geometric matching. Results using a linear SVM for classification along with SIFT, FFT-magnitude and DCT-based local feature descriptors indicate that the use of a single element from the dictionary to describe the local features is sufficient for accurate target classification. Continuing work both in feature extraction and classification will be discussed, with emphasis placed on the need for classification amid heavy target occlusion.