基于贝叶斯自组织映射和高斯混合模型的混合像元分解

提出一种新的对多通道遥感图像进行混合像元分解的方法.该方法将贝叶斯自组织映射算法引入混合像元分解问题中,通过最小化Kullback-Leibler信息度实现高斯参数的估计,并结合高斯混合模型完成解混.为了获得较高的解混精度,要求适当地扩展正态分布的范围,提出了3σ的方差调整方法来解决这一问题.所采用的解混模型自动满足混合像元分解问题所要求的2个约束条件:丰度值非负约束,丰度值和为1约束.实验结果表明,该方法有较好的混合像元分解结果,同时具有较强的抗噪声能力.

Decomposition of Mixed Pixels Based on Bayesian Self-Organizing Map and Gaussian Mixture Model

A new method for the decomposition of mixed pixels of multi-channel remote sensing images is proposed. The proposed method introduces the Bayesian self-organizing map into the problem of the decomposition of mixed pixels. It estimates the Gaussian parameters by minimizing the Kullback-Leibler information metric, and carries out the unmixing with the Gaussian mixture model. In order to obtain high unmixing precision, the effective range of Gaussian distributions needs to be extended, and the 3a variances adjustment method is adopted to solve the problem. In addition, the used unmixing model satisfies two constraints, abundances non-negative constraint and abundances summed-to-one constraint, which are demanded in automatical decomposition of mixed pixels. Experimental results show that the proposed method is able to obtain good unmixing results, and robust to noises.