  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.
