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.
翻译:在本文中,我们提议对遥感超光谱图像(HSI),即SP-DLRR(SP-DLRR)进行新的分类方案,全面探索其独特性,包括当地空间信息和低级别。SP-DLRR(SP-DLRR)主要由两个模块组成,即分类制超像素分解和歧视性低级别代表制,这些模块是迭接式的。具体来说,我们利用当地空间信息,并将典型分类器第一个模块像素,即第一个模块像素,即输入HSI(或由第二个模块产生的其恢复)到超级像素。根据由此产生的超像素,HSI的像素随后被分组并输入到我们新型的歧视性低等级代表制模型中,并具有有效的数字解决方案。这种模型能够通过抑制本地的光谱变异,同时促进全球的跨级相异性,导致以更具有歧视性的像素恢复HSI(或由第二个模块产生的其恢复)。在三个基准数据集上的实验结果显示SP-DRRix(特别具有超度的PRRRix)的办案数的极为高级的PSR-RRUS。