Hopfield Neural Network Approach for Supervised Nonlinear Spectral Unmixing.

Nonlinear unmixing, which has attracted considerable interest from researchers and developers, has been successfully applied in many real-world hyperspectral imaging scenarios. Hopfield neural network (HNN) machine learning has already proven successful in solving the linear mixture model; this study utilized an HNN machine learning approach to solve the generalized bilinear model (GBM) optimization problem. Two HNNs were constructed in a successive manner to solve respective seminonnegative matrix factorization problems intended for abundance and nonlinear coefficient estimation. In the proposed HNN-based GBM unmixing method, both HNNs evolve to stable states after a number of iterations to obtain unmixing results related to the states of neurons. In experiments on synthetic data, the proposed method showed more efficient performance in regard to abundance estimation accuracy than other GBM optimization algorithms, especially when given reliable endmember spectra. The proposed method was also applied to real hyperspectral data and still demonstrated notable advantages despite the obvious increase in unmixing difficulty. [ABSTRACT FROM PUBLISHER]