A class-wise spatial-contextual approach based on a free discontinuity model for change detection in multispectral images

The increased radiometric resolution of last generation multispectral sensors results in large statistical variability of classes represented in the image. However, classes present high spatial homogeneity. To preserve classes identity while simplifying their representation, in this paper we propose a class-wise spatial-contextual method based on a variational model with free discontinuities that reduces the statistical variability of classes by emphasizing their spatial contours. To prove its effectiveness, the proposed method is applied in the context of change detection in multispectral images. Here, it is able to augment the discrimination between the unchange and the change classes and to improve the detection performance.