Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification
Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification
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DOI：
10.3390/rs9090872
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
Fei Tong;Hengjian Tong;Junjun Jiang;Yun Zhang
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文献类型：
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作者：
Fei Tong;Hengjian Tong;Junjun Jiang;Yun Zhang
稀疏表示法已广泛应用于高光谱图像的分类。除了光谱信息外，HSI的空间背景在分类中也起着重要的作用。最近提出的多尺度自适应稀疏表示(MASR)分类器在利用空间信息进行HSI分类方面表现出了良好的性能。但空间信息是由固定大小的正方形窗口的多尺度斑块来利用的。该补丁可以包括所有最近的相邻像素，但这些相邻像素可能包含一些噪声像素。然后，另一项研究提出了一种基于超像素的多尺度稀疏表示(MSSR)分类器。形状自适应的超像素可以提供比面片更精确的表示。但很难为超像素选择比例。因此，受多尺度面片和超像素的优缺点启发，我们提出了一种多尺度联合区域自适应稀疏表示(MURASR)算法。联合区域是面片和超像素的重叠区域，可以充分利用两者的优点，克服各自的缺点。在多个HSI数据集上的实验表明，MURASR算法在稀疏表示上优于MASR算法，联合区域在稀疏表示上优于面片算法。
Sparse Representation has been widely applied to classification of hyperspectral images (HSIs). Besides spectral information, the spatial context in HSIs also plays an important role in the classification. The recently published Multiscale Adaptive Sparse Representation (MASR) classifier has shown good performance in exploiting spatial information for HSI classification. But the spatial information is exploited by multiscale patches with fixed sizes of square windows. The patch can include all nearest neighbor pixels but these neighbor pixels may contain some noise pixels. Then another research proposed a Multiscale Superpixel-Based Sparse Representation (MSSR) classifier. Shape-adaptive superpixels can provide more accurate representation than patches. But it is difficult to select scales for superpixels. Therefore, inspired by the merits and demerits of multiscale patches and superpixels, we propose a novel algorithm called Multiscale Union Regions Adaptive Sparse Representation (MURASR). The union region, which is the overlap of patch and superpixel, can make full use of the advantages of both and overcome the weaknesses of each one. Experiments on several HSI datasets demonstrate that the proposed MURASR is superior to MASR and union region is better than the patch in the sparse representation.