Change detection for remote sensing images with graph cuts

Fernando Pérez Nava; Alejandro Pérez Nava; José M. Gálvez Lamolda; Marcos Fuentes Redondo

doi:10.1117/12.627675

18 October 2005 Change detection for remote sensing images with graph cuts

Fernando Pérez Nava, Alejandro Pérez Nava, José M. Gálvez Lamolda, Marcos Fuentes Redondo

Proceedings Volume 5982, Image and Signal Processing for Remote Sensing XI; 59820Q (2005) https://doi.org/10.1117/12.627675
Event: SPIE Remote Sensing, 2005, Bruges, Belgium

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.

Citation Download Citation

Fernando Pérez Nava, Alejandro Pérez Nava, José M. Gálvez Lamolda, and Marcos Fuentes Redondo "Change detection for remote sensing images with graph cuts", Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820Q (18 October 2005); https://doi.org/10.1117/12.627675

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