Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification
Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification
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DOI：
10.3390/rs9020139
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发表时间：
2017-02
期刊：
影响因子：
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通讯作者：
Shuzhen Zhang;Shutao Li;Wei Fu;Leyuan Fang
中科院分区：
文献类型：
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作者：
Shuzhen Zhang;Shutao Li;Wei Fu;Leyuan Fang
近年来，超像素分割已被证明是高光谱图像（HSI）分类的有力工具。尽管如此，最佳超像素尺寸的选择是一项重要的任务。此外，与单尺度超像素分割相比，同一图像在不同尺度上分割可以获得不同的结构信息。为了克服这样的缺点，也利用结构信息，多尺度超像素的稀疏表示（MSSR）算法的HSI分类提出。具体而言，一种改进的多尺度超像素分割策略首次应用于HSI。一旦获得不同尺度上的超像素，联合稀疏表示分类被用于多尺度超像素分类。利用多数投票法对不同尺度超像素的标签进行融合，得到最终的分类结果。MSSR实现了两个优点。首先，多尺度信息融合可以更有效地挖掘HSI的空间信息。第二，在多尺度超像素分割中，除了第一尺度之外，不同HSI数据集的不同尺度上的超像素数量可以基于相应HSI的空间复杂度自适应地改变。在4个真实的HSI数据集上的实验表明，该算法在定性和定量上均优于几种著名的分类器.
Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale can obtain different structure information. To overcome such a drawback also utilizing the structural information, a multiscale superpixel-based sparse representation (MSSR) algorithm for the HSI classification is proposed. Specifically, a modified segmentation strategy of multiscale superpixels is firstly applied on the HSI. Once the superpixels on different scales are obtained, the joint sparse representation classification is used to classify the multiscale superpixels. Furthermore, majority voting is utilized to fuse the labels of different scale superpixels and to obtain the final classification result. Two merits are realized by the MSSR. First, multiscale information fusion can more effectively explore the spatial information of HSI. Second, in the multiscale superpixel segmentation, except for the first scale, the superpixel number on a different scale for different HSI datasets can be adaptively changed based on the spatial complexity of the corresponding HSI. Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed MSSR algorithm over several well-known classifiers.