IEEE Trans Image Process. 2016 Mar;25(3):1136-51. doi: 10.1109/TIP.2015.2509258. Epub 2015 Dec 17.
Mixing phenomena in hyperspectral images depend on a variety of factors, such as the resolution of observation devices, the properties of materials, and how these materials interact with incident light in the scene. Different parametric and nonparametric models have been considered to address hyperspectral unmixing problems. The simplest one is the linear mixing model. Nevertheless, it has been recognized that the mixing phenomena can also be nonlinear. The corresponding nonlinear analysis techniques are necessarily more challenging and complex than those employed for linear unmixing. Within this context, it makes sense to detect the nonlinearly mixed pixels in an image prior to its analysis, and then employ the simplest possible unmixing technique to analyze each pixel. In this paper, we propose a technique for detecting nonlinearly mixed pixels. The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model. The two errors are combined into a detection statistics for which a probability density function can be reasonably approximated. We also propose an iterative endmember extraction algorithm to be employed in combination with the detection algorithm. The proposed detect-then-unmix strategy, which consists of extracting endmembers, detecting nonlinearly mixed pixels and unmixing, is tested with synthetic and real images.
高光谱图像中的混合现象取决于多种因素，例如观测设备的分辨率、材料的性质以及这些材料在场景中与入射光的相互作用方式。已经考虑了不同的参数和非参数模型来解决高光谱解混问题。最简单的是线性混合模型。然而，已经认识到混合现象也可能是非线性的。相应的非线性分析技术比线性解混所采用的技术必然更具挑战性和复杂性。在这种情况下，在对图像进行分析之前，有必要先检测图像中的非线性混合像素，然后使用最简单的解混技术来分析每个像素。在本文中，我们提出了一种用于检测非线性混合像素的技术。该检测方法基于使用高斯过程回归模型和线性回归模型对重建误差的比较。将两个误差组合成一个检测统计量，该统计量可以合理地近似其概率密度函数。我们还提出了一种迭代端元提取算法，与检测算法结合使用。所提出的检测-然后-解混策略包括提取端元、检测非线性混合像素和解混，并用合成图像和真实图像进行了测试。
