Yang Shujun, Hou Junhui, Jia Yuheng, Mei Shaohui, Du Qian
IEEE Trans Image Process. 2021;30:8823-8835. doi: 10.1109/TIP.2021.3120675. Epub 2021 Oct 27.
In this paper, we propose a novel classification scheme for the remotely sensed hyperspectral image (HSI), namely SP-DLRR, by comprehensively exploring its unique characteristics, including the local spatial information and low-rankness. SP-DLRR is mainly composed of two modules, i.e., the classification-guided superpixel segmentation and the discriminative low-rank representation, which are iteratively conducted. Specifically, by utilizing the local spatial information and incorporating the predictions from a typical classifier, the first module segments pixels of an input HSI (or its restoration generated by the second module) into superpixels. According to the resulting superpixels, the pixels of the input HSI are then grouped into clusters and fed into our novel discriminative low-rank representation model with an effective numerical solution. Such a model is capable of increasing the intra-class similarity by suppressing the spectral variations locally while promoting the inter-class discriminability globally, leading to a restored HSI with more discriminative pixels. Experimental results on three benchmark datasets demonstrate the significant superiority of SP-DLRR over state-of-the-art methods, especially for the case with an extremely limited number of training pixels.
在本文中，我们通过全面探索遥感高光谱图像（HSI）的独特特性，包括局部空间信息和低秩性，提出了一种新颖的分类方案，即SP-DLRR。SP-DLRR主要由两个模块组成，即分类引导的超像素分割和判别性低秩表示，这两个模块是迭代进行的。具体来说，通过利用局部空间信息并结合典型分类器的预测，第一个模块将输入HSI（或由第二个模块生成的其恢复图像）的像素分割为超像素。根据得到的超像素，然后将输入HSI的像素分组为簇，并输入到我们具有有效数值解的新颖判别性低秩表示模型中。这样的模型能够通过局部抑制光谱变化来增加类内相似度，同时全局促进类间可区分性，从而得到具有更多可区分像素的恢复后的HSI。在三个基准数据集上的实验结果证明了SP-DLRR相对于现有方法的显著优越性，特别是对于训练像素数量极其有限的情况。
