Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images

Abstract

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 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.

References in corpus (2)

• Blind and fully constrained unmixing of hyperspectral images
• Estimating the Intrinsic Dimension of Hyperspectral Images Using an Eigen-Gap Approach

Cited by in corpus (11)

• Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing
• Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
• A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing
• Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
• A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability
• Technical Report: Band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem
• Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis
• A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing
• Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
• Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor
• Using Spatial Correlation in Semi-Supervised Hyperspectral Unmixing under Polynomial Post-nonlinear Mixing Model