This document summarizes a proposed method for interactive change detection in multispectral remote sensing images. The method uses user-provided markers for change and no-change classes to train a support vector machine classifier, generating a spectral change map. Two approaches are described to incorporate spatial context - a Markov random field method that is region-driven, and a level-set method that exploits both regions and contours. Experiments on real remote sensing images showing different types of changes demonstrate that the interactive methods can accurately generate change detection maps with simple minimal user interaction.

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INTERACTIVE SEGMENTATION FOR CHANGE DETECTION IN
MULTISPECTRAL REMOTE-SENSING IMAGES
ABSTRACT:
We propose to solve the change detection (CD) problem in multitemporal remote-sensing images
using interactive segmentation methods. The user needs to input markers related to change and
no-change classes in the difference image. Then, the pixels under these markers are used by the
support vector machine classifier to generate a spectral-change map. To enhance further the
result, we include the spatial contextual information in the decision process using two different
solutions based on Markov random field and level-set methods. While the former is a regiondriven method, the latter exploits both region and contour for performing the segmentation task.
Experiments conducted on a set of four real remote-sensing images acquired by low as well as
very high spatial resolution sensors and referring to different kinds of changes confirm the
attractive capabilities of the proposed methods in generating accurate CD maps with simple and
minimal interaction.