Partially supervised detection of changes from remote sensing images

Given a temporal sequence of remote sensing images, the difficulty to collect regularly in time ground truth information makes it important to develop automatic unsupervised change detection techniques. Due to its simplicity, image differencing represents a popular approach for change detection. To separate the "change" and "no-change" classes in the difference image, a thresholding-based procedure can be applied. However, the main weakness of an unsupervised change detection approach is the absence of prior information about the scene as it resorts to the spectral information, only, which does not allow the analysis of the typologies of changes occurring between the acquisition dates. In the monitoring of a given study area, the main problem is that the ground truth collection does not usually follow the image acquisitions at the different dates. However, it is easier to have at least one image for which the ground truth is available. In this paper, we propose a partially supervised change detection scheme that is based on the exploitation of the ground truth availability for at least one temporal image. A clustering algorithm is applied to both acquired images and a thresholding-based unsupervised change detection algorithm is applied inside each cluster of the second date image in order not only to identify the presence of changes but also to distinguish between different typologies of changes.