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Data-augmented matched subspace detector for hyperspectral subpixel target detection

Yang, X; Dong, M; Wang, Z; Gao, L; Zhang, L; Xue, J-H; (2020) Data-augmented matched subspace detector for hyperspectral subpixel target detection. Pattern Recognition , 106 , Article 107464. 10.1016/j.patcog.2020.107464. Green open access

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Abstract

The performance of subspace-based methods such as matched subspace detector (MSD) and MSD with interaction effects (MSDinter) heavily depends on the background subspace and the target subspace. Nonetheless, constructing a representative target subspace is challenging due to the limited availability of target spectra in a collected hyperspectral image. In this paper, we propose two new hyperspectral target detection methods termed data-augmented MSD (DAMSD) and data-augmented MSDinter (DAMSDI) that can effectively solve the scarcity problem of target spectra and from which a representative target-background mixed subspace can be learned. We first synthesise target-background mixed spectra based on classical hyperspectral mixing models and then learn a target-background mixed subspace via principal component analysis. Compared with MSD and MSDinter, the learned mixed subspace is more representative as spectral variability of target spectra is explained to the largest extent and it leads to an improvement in computational speed and numerical stability. We demonstrate the efficacy of DAMSD and DAMSDI for subpixel target detection on two public hyperspectral image datasets.

Type: Article
Title: Data-augmented matched subspace detector for hyperspectral subpixel target detection
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.patcog.2020.107464
Publisher version: https://doi.org/10.1016/j.patcog.2020.107464
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Hyperspectral imaging, Matched subspace detector (MSD), Subpixel target detection, Data augmentation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10098372
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