Classifying multi-temporal TM imagery using Markov Random Fields and Support Vector Machines

Abstract

In this paper, we propose a spatial-temporally explicit algorithm to simultaneously classify multi-temporal images for land cover information. This algorithm has three steps: first, a machine learning algorithm Support Vector Machines (SVM), is trained with spectral observations to initialize the classification and estimate pixel-by-pixel class conditional probabilities for each individual image; second, Markov Random Fields (MRF) are used to model the spatial-temporal contextual prior probabilities of images; and finally, an iterative algorithm is used to update the classification based on the combination of the spectral class conditional probability and the spatial-temporal contextual prior probability. Increased accuracies from the contributions of spatial-temporal contextual evidence confirmed the importance of spatial-temporal modeling in multi-temporal remote sensing. © 2005 IEEE.