Multispectral Remote Sensing Image Classification Model Based on Probabilistic Diffusion
基于概率扩散的多光谱遥感图像分类模型

In this paper,we propose an automatic multispectral remote sensing image classification technique based on improved probabilistic diffusion.Firstly,the optimal number of clusters in multispectral images is determined by comparing the validity functions of fuzzy c-means classifier(FCM).The posterior probability maps for each class are then smoothed by an improved version of multispectral anisotropic diffusion based on morphology.Finally,each pixel is classified independently using the maximum a posterior probability(MAP) estimate based on probabilistic membership maps.Because of the elegant property of anisotropic diffusion,edge-preserving smoothing,probabilistic diffusion,not only restrains effectively speckles in homogeneous regions,but also preserves preferably the significant physiognomy and edge features.Experimental results are given to show that the proposed method avoids the influence of "class noise" and its overall accuracy and Kappa coefficient have superiority capability over the traditional maximum a posterior probability estimate classification method without probabilistic diffusion.Thus it is an ideal remote sensing classification method.