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SUPERPIXEL CLASSIFICATION METHOD BASED ON SEMI-SUPERVISED K-SVD AND MULTISCALE SPARSE REPRESENTATION

机译:基于半监督K-SVD和多尺度稀疏表示的超像素分类方法

摘要

The present invention discloses a superpixel classification method based on semi-supervised K-SVD and multiscale sparse representation. The method includes carrying out semi-supervised K-SVD dictionary learning on the training samples of a hyperspectral image; using the training samples and the overcomplete dictionary as the input to obtain the multiscale sparse solution of superpixels; and using the obtained sparse representation coefficient matrix and overcomplete dictionary to obtain the result of superpixel classification by residual method and superpixel voting mechanism. The proposing of the present invention is of great significance to solving the problem of salt and pepper noise and the problem of high dimension and small samples in the field of hyperspectral image classification, as well as the problem of how to effectively use space information in classification algorithm based on sparse representation.
机译:本发明公开了一种基于半监督K-SVD和多尺度稀疏表示的超像素分类方法。该方法包括对高光谱图像的训练样本进行半监督的K-SVD字典学习。使用训练样本和超完备字典作为输入以获得超像素的多尺度稀疏解;利用获得的稀疏表示系数矩阵和超完备字典,通过残差法和超像素投票机制获得超像素分类结果。本发明的提出对于解决高光谱图像分类领域中的椒盐噪声问题,高维小样本问题以及如何有效利用空间信息进行分类问题具有重要意义。稀疏表示的算法

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