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.

中文翻译：

---

用于高光谱图像分类的具有形状自适应区域和局部自适应字典的联合协作表示

在这封信中，提出了一种新的基于形状自适应区域和局部自适应字典（SALJCR）联合协同表示的高光谱图像（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空间信息。对整个图像执行平均滤波（根据 SA 区域）。然后，基于过滤后的图像，为每个测试像素构建一个局部自适应字典，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。