本文首先通过理论分析,探讨了高斯过程分类器在高分辨率遥感图像变化检测应用中的优势与不足,并针对高斯过程分类器的不足给出了相应的解决方法;其次,提出了一种基于空间上下文相关的高斯过程变化检测方法;最后,通过多个高分辨率遥感实验数据集上的实验设计与分析,验证了高斯过程分类器在高分辨率遥感图像变化检测中的应用能力,并证明了本文提出的解决方法的有效性.

Gaussian process (GP) represents a powerful theoretical framework for Bayesian classif ication. Despite GP classifier have gained prominence in recent years, it remains an approach whose potentialities are not yet suff iciently known in remote sensing community. This paper gives a thorough investigation of GP CLASSIFIER for high resolution (HR) multi-temporal image change detection. Firstly, we give a detailed analysis of the capabilities of GP classif ier in theory. Secondly, we elaborately explore the advantages and disadvantages of the GP classif iers. Finally, we design several experiments to test the performance of the GP classif ier for HR remote sensing image change detection. Moreover, we propose a novel approach for improving the capacities of GP classif ier in remote sensing image change detection. The proposed context-sensitive change detection method is achieved by analyzing the posterior probability of probabilistic GP classif ier within a markov random f ield (MRF) framework. In particular, the method consists of two steps: (1) A supervised initialization is founded on a probabilistic GP classif ier; (2) A MRF regularization aims at ref ining the posterior probability by employing the spatial context information. Five experiments carried out on HR remote sensing image set validate the power of GP classif ier for change detection and also the effectiveness of our proposed methods.