An Automatic Approach to Adaptive Local Background Estimation and Suppression in Hyperspectral Target Detection
An Automatic Approach to Adaptive Local Background Estimation and Suppression in Hyperspectral Target Detection
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DOI：
10.1109/tgrs.2010.2065235
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发表时间：
2011-02
影响因子：
8.2
通讯作者：
S. Matteoli;N. Acito;M. Diani;G. Corsini
中科院分区：
文献类型：
--
作者：
S. Matteoli;N. Acito;M. Diani;G. Corsini
研究了基于子空间的高光谱图像目标检测方法。具体地说，它侧重于一个通用的检测方案，首先，背景抑制通过正交子空间投影，然后完成目标检测。背景子空间的充分估计对于成功的结果是必不可少的。背景子空间通常是全局估计的。然而，全球的方法可能是无效的小目标检测应用程序，因为他们往往高估了影响一个给定的目标的背景干扰。这可能导致背景抑制后的低目标残余能量，这对检测性能是有害的。在本文中，我们提出了一种新的和全自动算法的局部背景子空间估计（LBSE）。局部背景通常具有比全局背景更低的固有复杂度。通过在测试像素的局部邻域上估计背景子空间，期望得到的背景子空间维数较低，从而导致抑制后的更高的目标残余能量，这有利于检测性能。具体而言，建议的LBSE的行为上的每像素的基础上，从而自适应地剪裁估计的基础上的局部复杂性的背景。模拟和真实的高光谱数据调查LBSE提供的检测性能的改善与全球和当地的方法先前提出的。
This paper deals with subspace-based target detection in hyperspectral images. Specifically, it focuses on a general detection scheme where, first, background is suppressed through orthogonal-subspace projection and then target detection is accomplished. An adequate estimation of the background subspace is essential to a successful outcome. The background subspace has been typically estimated globally. However, global approaches may be ineffective for small-target-detection applications since they tend to overestimate the background interference affecting a given target. This may result in a low target residual energy after background suppression that is detrimental to detection performance. In this paper, we propose a novel and fully automatic algorithm for local background-subspace estimation (LBSE). Local background has typically a lower inherent complexity than that of global background. By estimating the background subspace over a local neighborhood of the test pixel, the resulting background-subspace dimension is expected to be low, thus resulting in a higher target residual energy after suppression which benefits the detection performance. Specifically, the proposed LBSE acts on a per-pixel basis, thus adaptively tailoring the estimated basis to the local complexity of background. Both simulated and real hyperspectral data are employed to investigate the detection-performance improvements offered by LBSE with respect to both global and local methodologies previously presented.