Paper Info
Title
Superpixel-Guided Discriminative Low-Rank Representation of Hyperspectral Images for Classification
Abstract
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.
Year
DOI
Venue
2021
10.1109/TIP.2021.3120675
IEEE TRANSACTIONS ON IMAGE PROCESSING
Keywords
DocType
Volume
Image segmentation, Image restoration, Numerical models, Tensors, Spectral analysis, Prediction algorithms, Hyperspectral imaging, Low-rank, superpixel segmentation, hyperspectral image, classification
Journal
30
Issue
ISSN
Citations 
1
1057-7149
0
PageRank 
References 
Authors
0.34
10
5
Name
Order
Citations
PageRank
Shujun Yang100.34
Junhui Hou239549.84
Yuheng Jia39313.13
Shaohui Mei4336.94
Qian Du58512.32