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Hyperspectral Target Detection Based on Weighted Cauchy Distance Graph and Local Adaptive Collaborative Representation by Xiaobin Zhao; Wei Li; Chunhui Zhao; Ran Tao is a Engineering article available to read on EtoBox.

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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 i

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Author

Xiaobin Zhao; Wei Li; Chunhui Zhao; Ran Tao

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)