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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Joint Collaborative Representation With Shape Adaptive Region and Locally Adaptive Dictionary for Hyperspectral Image Classification
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Joint Collaborative Representation With Shape Adaptive Region and Locally Adaptive Dictionary for Hyperspectral Image Classification

机译：具有形状自适应区域和局部自适应词典的联合协作表示，用于高光谱图像分类

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摘要

A novel hyperspectral image (HSI) classification method based on joint collaborative representation with shape adaptive region and locally adaptive dictionary (SALJCR) is proposed in this letter. First, the shape adaptive (SA) region is selected for each pixel to exploit the neighboring spatial information adaptively. The average filtering (according to SA regions) is performed for the whole image. Then, based on the filtered image, a locally adaptive dictionary is constructed for each test pixel to reduce the negative impact of irrelevant pixels on representation. Finally, a joint collaborative representation method is applied to decompose the pixels and assign the class label. Experimental results demonstrate that the proposed SALJCR method outperforms some state-of-the-art classifiers.
机译：在该字母中提出了一种基于与形状自适应区域和局部自适应词典（SALJCR）的关节协同表示的新颖的高光谱图像（HSI）分类方法。首先，为每个像素选择形状自适应（SA）区域以自适应地利用相邻的空间信息。为整个图像执行平均滤波（根据SA区域）。然后，基于滤波图像，为每个测试像素构建局部自适应词典，以减少不相关像素对表示的负面影响。最后，应用联合协同表示方法来分解像素并分配类标签。实验结果表明，所提出的SalJCR方法优于一些最先进的分类器。

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