IEEE Trans Image Process. 2017 Nov;26(11):5447-5461. doi: 10.1109/TIP.2017.2740621. Epub 2017 Aug 16.
Hyperspectral images (HSIs) possess non-negative properties for both hyperspectral signatures and abundance coefficients, which can be naturally modeled using cone-based representation. However, in hyperspectral target detection, cone-based methods are barely studied. In this paper, we propose a new regularized cone-based representation approach to hyperspectral target detection, as well as its two working models by incorporating into the cone representation l-norm and l-norm regularizations, respectively. We call the new approach the matched shrunken cone detector (MSCD). Also important, we provide principled derivations of the proposed MSCD from the Bayesian perspective: we show that MSCD can be derived by assuming a multivariate half-Gaussian distribution or a multivariate half-Laplace distribution as the prior distribution of the coefficients of the models. In the experimental studies, we compare the proposed MSCD with the subspace methods and the sparse representation-based methods for HSI target detection. Two real hyperspectral data sets are used for evaluating the detection performances on sub-pixel targets and full-pixel targets, respectively. Results show that the proposed MSCD can outperform other methods in both cases, demonstrating the competitiveness of the regularized cone-based representation.
高光谱图像（HSI）具有非负的高光谱特征和丰度系数，这可以通过基于锥的表示自然建模。然而，在高光谱目标检测中，基于锥的方法几乎没有被研究过。在本文中，我们提出了一种新的正则化基于锥的表示方法，用于高光谱目标检测，以及通过分别将 l-范数和 l-范数正则化纳入锥表示而得到的两个工作模型。我们将新方法称为匹配收缩锥检测器（MSCD）。同样重要的是，我们从贝叶斯的角度给出了所提出的 MSCD 的原理推导：我们表明，MSCD 可以通过假设多元半高斯分布或多元半拉普拉斯分布作为模型系数的先验分布来推导。在实验研究中，我们将所提出的 MSCD 与子空间方法和基于稀疏表示的方法进行了比较，用于 HSI 目标检测。使用两个真实的高光谱数据集分别评估子像素目标和全像素目标上的检测性能。结果表明，在这两种情况下，所提出的 MSCD 都优于其他方法，证明了正则化基于锥的表示的竞争力。
