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              <p>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.</p>
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