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This paper addresses the problem of land-cover maps updating by classifying multitemporal remote sensing images (i.e.,
images acquired on the same area at different times) in the context of change-detection-driven active transfer learning.
The proposed method is based on the assumption that training samples are available for one of the available
multitemporal images (i.e., source domain), whereas they are not for the others (i.e., target domain). In order to
effectively classify the target domain (i.e., update the maps obtained for the source domain according to the new
information brought from another acquisition) we present a novel approach to automatically define a training set for the
target domain taking advantage of its temporal correlation with the source domain. The proposed method is based on
four steps. In the first step unsupervised change detection is applied to multitemporal images (i.e., target and source
domains). Labels of detected unchanged training samples are propagated from the source to the target domain in the
second step, thus becoming its initial training set. In the third step, changed areas are statistically compared with land-cover
classes in the target domain training set. This information is used to drive the initial training set expansion by
Active Learning (AL). In the first expansion iterations priority is given to samples detected as being changed, in the next
ones the most informative samples are selected from a pool including both changed and unchanged unlabeled samples
(i.e., priority is removed). At convergence of the AL process, the target image is classified (fourth step). To this, in this
paper we use a Support Vector Machine classifier. Experimental results show that transferring the class-labels from
source domain to target domain provides a reliable initial training set and that the priority rule for AL involves a faster
convergence to the desired accuracy with respect to standard AL.
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