Hyperspectral Image Classification via Sparse Representation With Incremental Dictionaries

Shujun Yang, Junhui Hou*, Yuheng Jia, Shaohui Mei, Qian Du

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

12 Citations (Scopus)

Abstract

In this letter, we propose a new sparse representation (SR)-based method for hyperspectral image (HSI) classification, namely SR with incremental dictionaries (SRID). Our SRID boosts existing SR-based HSI classification methods significantly, especially when used for the task with extremely limited training samples. Specifically, by exploiting unlabeled pixels with spatial information and multiple-feature-based SR classifiers, we select and add some of them to dictionaries in an iterative manner, such that the representation abilities of the dictionaries are progressively augmented, and likewise more discriminative representations. In addition, to deal with largescale data sets, we use a certainty sampling strategy to control the sizes of the dictionaries, such that the computational complexity is well balanced. Experiments over two benchmark data sets show that our proposed method achieves higher classification accuracy than the state-of-the-art methods, i.e., the overall classification accuracy can improve more than 4%.
Original languageEnglish
Article number8892586
Pages (from-to)1598-1602
JournalIEEE Geoscience and Remote Sensing Letters
Volume17
Issue number9
Online published6 Nov 2019
DOIs
Publication statusPublished - Sept 2020

Research Keywords

  • Hyperspectral image (HSI) classification
  • incremental learning
  • multiple features
  • sparse representation (SR)

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