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Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building(More)
This paper presents a novel unsupervised clustering scheme to find changes in two or more coregistered remote sensing images acquired at different times. This method is able to find nonlinear boundaries to the change detection problem by exploiting a kernel-based clustering algorithm. The kernel k-means algorithm is used in order to cluster the two groups(More)
provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsuper-vised methods and the use of multi-modal data for flood detection.(More)
Traditionally, land-cover mapping from remote sensing images is performed by classifying each atomic region in the image in isolation and by enforcing simple smoothing priors via random fields models as two independent steps. In this paper, we propose to model the segmentation problem by a discriminatively trained Conditional Random Field (CRF). To this(More)