Decomposition of mixed pixels based on bayesian self-organizing map and Gaussian mixture model

How to decompose the mixed pixels precisely and effectively for multispectral/hyperspectral remote sensing images is a critical issue for the quantitative remote sensing research. This paper proposes a new method for decomposition of mixed pixels of multispectral/hyperspectral remote sensing images. The proposed method introduces the algorithm of Bayesian self-organizing map (BSOM) into the problem of the decomposition of mixed pixels. It estimates Gaussian parameters by minimizing the Kullback-Leibler information metric, and finishes the unmixing with Gaussian mixture model (GMM). In order to obtain a high unmixing precision, we need to extend the range of Gaussian distributions, and thus we propose a 3σ variance adjustment method to solve this problem. In addition, the proposed unmixing model automatically satisfies two constraints which are demanded for the problem of the decomposition of mixed pixels: abundances non-negative constraint (ANC) and abundances summed-to-one constraint (ASC). Experimental results on simulated and practical remote sensing images demonstrate that the proposed method can get good unmixing results for the decomposition of mixed pixels and is more robust to noise than other methods.