Decomposition of mixed pixels using Bayesian Self- Organizing Map (BSOM) neural networks

How to decompose mixed pixels effectively and precisely for remote sensing images is a critical issue for the quantitative remote sensing research. In this paper we propose a new method for decomposition of mixed pixels of multispectral or 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 propose the 3 sigma variances adjustment method to solve this problem. In addition, the used unmixing model satisfies two constraints which are demanded for the problem of the decomposition of mixed pixels automatically: 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.