One hyperspectral object detection algorithm for solving spectral variability problems of the same object in different conditions

Lei Yu; Jinhui Lan; Yiliang Zeng; Jinlin Zou; Bingqing Niu

doi:10.1117/1.JRS.13.026514

19 June 2019 One hyperspectral object detection algorithm for solving spectral variability problems of the same object in different conditions

Lei Yu, Jinhui Lan, Yiliang Zeng, Jinlin Zou, Bingqing Niu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 2, 026514 (June 2019). https://doi.org/10.1117/1.JRS.13.026514

Abstract

Object detection from hyperspectral images (HSIs) is an important issue but encounters a critical challenge that results in poor detection due to the variation of the detection object spectrum. Especially when the detection object area is large and widely distributed in HSIs, such spectral variability becomes more serious. Spectral variability can make false detection and leak detection in object detection very serious. The constrained energy minimization (CEM) algorithm is a classical object detection algorithm that only needs the object prior spectrum to achieve object detection, but the spectral variability will have a detrimental effect on the detection results of the CEM algorithm. To address the above problems, we propose a multiobject subspace projection sample weighted CEM (MSPSW-CEM) algorithm. The proposed method has the following capabilities: (1) it constructs object subspaces and detectors using multiple prior spectra of the detection object under spectral variability conditions and (2) it utilizes the subspace projection theory to weight the pixel spectra, so that the detector can better suppress the background information and highlight the object information. Extensive experiments were carried out on two sets of real-world HSIs, and it was found that MSPSW-CEM generally showed a better detection performance than other object detection methods.

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Lei Yu, Jinhui Lan, Yiliang Zeng, Jinlin Zou, and Bingqing Niu "One hyperspectral object detection algorithm for solving spectral variability problems of the same object in different conditions," Journal of Applied Remote Sensing 13(2), 026514 (19 June 2019). https://doi.org/10.1117/1.JRS.13.026514

Received: 25 December 2018; Accepted: 13 May 2019; Published: 19 June 2019

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