Share Email Print

Proceedings Paper

A change-detection-driven approach to active transfer learning for classification of image time series
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 26 October 2011
PDF: 11 pages
Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800E (26 October 2011); doi: 10.1117/12.898596
Show Author Affiliations
Begüm Demir, Univ. of Trento (Italy)
Francesca Bovolo, Univ. of Trento (Italy)
Lorenzo Bruzzone, Univ. of Trento (Italy)

Published in SPIE Proceedings Vol. 8180:
Image and Signal Processing for Remote Sensing XVII
Lorenzo Bruzzone, Editor(s)

© SPIE. Terms of Use
Back to Top