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Background Learning Based on Target Suppression Constraint for Hyperspectral Target Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2020-01-01 , DOI: 10.1109/jstars.2020.3024903
Weiying Xie , Xin Zhang , Yunsong Li , Keyan Wang , Qian Du

Hyperspectral target detection is critical in both military and civilian applications. However, it is a challenging task due to the complexity of background and the limited samples of target in hyperspectral images (HSIs). In this article, we propose a novel background learning model, called background learning based on target suppression constraint to characterize high-dimensional spectral vectors. Considering insufficient target samples, the model is trained only on the background spectral samples to accurately learn the background distribution. Then the discrepancy between the reconstructed and original HSIs are examined to spot the targets. To obtain a background training dataset, coarse detection is carried out. However, it is quite difficult to retrieve pure background data. Thus, a target suppression constraint is imposed to reduce the impact of suspected target samples on background reconstruction. Experiments on six real HSIs demonstrate that the proposed framework significantly outperforms the current state-of-the-art detection methods and yields higher detection accuracy and lower false alarm rate.

中文翻译:

基于目标抑制约束的背景学习高光谱目标检测

高光谱目标检测在军事和民用应用中都至关重要。然而,由于背景的复杂性和高光谱图像(HSI)中目标的有限样本,这是一项具有挑战性的任务。在本文中,我们提出了一种新的背景学习模型,称为基于目标抑制约束的背景学习来表征高维谱向量。考虑到目标样本不足,模型只对背景光谱样本进行训练,以准确学习背景分布。然后检查重建的 HSI 和原始 HSI 之间的差异以发现目标。为了获得背景训练数据集,进行粗检测。然而,检索纯背景数据是相当困难的。因此,施加目标抑制约束以减少可疑目标样本对背景重建的影响。在六个真实 HSI 上的实验表明,所提出的框架显着优于当前最先进的检测方法,并产生更高的检测精度和更低的误报率。
更新日期:2020-01-01
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