Change detection for remote sensing images with graph cuts

Abstract

Change detection is an important part of many remote sensing applications. This paper addresses the problem of unsupervised pixel classification into 'Change' and 'No Change' classes based on Hidden Markov Random Field (HMRF) models. HMRF models have long been recognized as a method to enforce spatially coherent class assignment. The optimal classification under these models is usually obtained under the Maximum a Posteriori (MAP) criterion. However, the MAP classification in HMRF models leads in general to problems with exponential complexity, so approximate techniques are needed. In this paper we show that the simple structure of the change detection problem makes that MAP classification can be exactly and efficiently calculated using graph cut techniques. Another problem related to HMRF modelling (and to any change detection technique) is the determination of the parameters or thresholds for classification. This learning problem is solved in our HMRF model by the Expectation Maximization (EM) algorithm. Experimental results obtained on four sets of multispectral remote sensing images confirm the validity of the proposed approach.