Hyperspectral target detection method based on filter tensor analysis

doi:10.7523/j.ucas.2020.0060

Abstract

Abstract: Most of the existing target detection algorithms for hyperspectral images treat each band indiscriminately, so the physical information of the image band cannot be fully utilized. In this paper, the hyperspectral images are firstly divided into several different waveband ranges (such as visible light, near infrared, shortwave infrared, etc.) according to the different imaging mechanisms. A recently developed multi-temporal target detection algorithm:FTA (filter tensor analysis) is introduced into the hyperspectral target detection by combining the different waveband ranges of the hyperspectral images with the time-phase dimension of multi-temporal remote sensing data correspondingly. Based on the new approach, a band-divided FTA algorithm for single-temporal hyperspectral images is proposed. Experiments on hyperspectral images prove that the band-divided FTA algorithm can achieve better results in target detection than the traditional single-phase target detection algorithm.

Key words: hyperspectral image, target detection, FTA, band-divided, CEM

CLC Number:

TP751.1

YANG Shuai, JI Luyan, GENG Xiurui. Hyperspectral target detection method based on filter tensor analysis[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(5): 684-694.

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