Global-Local Balanced Low-Rank Approximation of Hyperspectral Images for Classification

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Number of pages12
Journal / PublicationIEEE Transactions on Circuits and Systems for Video Technology
Online published7 Jul 2021
Publication statusOnline published - 7 Jul 2021

Abstract

This paper explores the problem of recovering the discriminative representation of a hyperspectral remote sensing image (HRSI), which suffers from spectral variations, to boost its classification accuracy. To tackle this challenge, we propose a new method, namely local-global balanced low-rank approximation (GLB-LRA), which can increase the similarity between pixels belonging to an identical category while promoting the discriminability between pixels of different categories. Specifically, by taking advantage of the particular structural spatial information of HRSIs, we exploit the low-rankness of an HRSI robustly in both spatial and spectral domains from the perspective of local and global balance. We mathematically formulate GLB-LRA as an explicit optimization problem and propose an iterative algorithm to solve it efficiently. Experimental results over three commonly-used benchmark datasets demonstrate the significant superiority of our method over state-of-the-art methods.

Research Area(s)

  • classification, Computational modeling, Dimensionality reduction, Hyperspectral image, Hyperspectral imaging, Imaging, low-rank, Optimization, spectral variation, Tensors, Three-dimensional displays