Hyperspectral Target Detection Based on Weighted Cauchy Distance Graph and Local Adaptive Collaborative Representation

Xiaobin Zhao, Wei Li*, Chunhui Zhao, Ran Tao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

Hyperspectral target detection in complex backgrounds is a challenging and important research topic in the remote sensing field. Traditional target detectors consider the background spectrum to obey a Gaussian distribution. However, this distribution may not meet the requirements in real hyperspectral images. In addition, the background and spatial information of most existing target detection algorithms are rarely fully utilized. Therefore, a new weighted Cauchy distance graph (WCDG) and local adaptive collaborative representation detection (CGCRD) is proposed. First, a WCDG similarity measure is designed. In order to adjust the effect of target pixels on the graph model, a weighted Cauchy distance Laplace matrix is constructed, and then the matrix is applied to the matched filter detector. Second, local adaptive collaborative representation strategy is developed. The penalty coefficient is weighted by the local spatial Euclidean distance combined with the Pearson correlation coefficient, and then the detection result is obtained based on the residual. Finally, aforementioned two strategies are fused to fully utilize the spatial and spectral information. A 176-band hyperspectral image (BIT-HSI-I) dataset is collected for the target detection task. The related algorithms are performed on the BIT-HSI-I dataset, and the detection results demonstrate that the proposed algorithm has better detection performance than other state-of-the-art algorithms.

Original languageEnglish
Article number5527313
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
DOIs
Publication statusPublished - 2022

Keywords

  • Euclidean distance
  • Pearson correlation coefficient
  • hyperspectral target detection
  • local adaptive collaborative representation
  • matched filter
  • weighted Cauchy distance

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