Data-augmented matched subspace detector for hyperspectral subpixel target detection

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

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.

• Item Type: Articles
• Additional Information: This work was partly supported by the Royal Society under Royal Society-Newton Mobility Grant IE161194, and by the National Natural Science Foundation of China under Grant 61711530239.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Yang, Dr Xiaochen
• Authors: Yang, X., Dong, M., Wang, Z., Gao, L., Zhang, L., and Xue, J.-H.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Pattern Recognition
• Publisher: Elsevier
• ISSN: 0031-3203
• ISSN (Online): 1873-5142
• Published Online: 24 May 2020
• Copyright Holders: Copyright © 2020 Elsevier Ltd.
• First Published: First published in Pattern Recognition 106: 107464
• Publisher Policy: Reproduced in accordance with the publisher copyright policy