MRF和GM模型高光谱图像地物标记

Hyperspectral Image Labeling Using MRF and GM Models

利用马尔可夫随机场和高斯混合模型构造了一种对高光谱图像进行地物标记的新方法。该方法利用PCA降维后的高光谱图像及其差分图像的先验信息建立高光谱图像的随机模型,并把最大后验估计作为地物标记优化的评判标准,用模拟退火算法实现地物标记。实验结果显示该算法是一种精确、高效、稳定的图形标记算法。

Based on Markov Random Fields （MRF） and Gaussian Mixture （GM） models, a new method to label surface features using hyperspectral imaging is presented. The dimension of the hyperspectral image is reduced by PCA, and the stochastic model is built based on prior of the dimension-reduced images and its difference images. Then the maximum posteriori is designed as the optimal criterion and the final labels are obtained by the simulated annealing algorithm. Experimental results show that this method is accurate, efficient and robust for surface features labeling.