Translator Disclaimer
22 October 2010 A novel approach to land-cover maps updating in complex scenarios based on multitemporal remote sensing images
Author Affiliations +
Nowadays, an ever increasing number of multi-temporal images is available, giving the possibility of having with high temporal frequency information about the land-cover evolution on the ground. In general, the production of accurate land-cover maps requires the availability of reliable ground truth information on the considered area for each image to be classified. Unfortunately the rate of ground truth information collection will never equal the remote sensing image acquisition rate, making supervised classification unfeasible for land-cover maps updating. This problem has been faced according to domain adaptation methods that update land-cover maps under the assumption that: i) training data are available for one of the considered multi-temporal acquisitions while they are not for the others and ii) set of land-cover classes is same for all considered acquisitions. In real applications, the latter assumption represents a constraint which is often not satisfied due to possible changes occurred on the ground and associated with the presence of new classes or the absence of old classes in the new images. In this work, we propose an approach that removes this constraint by automatically identifying whether there exist differences between classes in multi-temporal images and properly handling these differences in the updating process. Experimental results on a real multi-temporal remote sensing data set confirm the effectiveness and the reliability of the proposed approach.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
K. Bahirat, F. Bovolo, L. Bruzzone, and S. Chaudhuri "A novel approach to land-cover maps updating in complex scenarios based on multitemporal remote sensing images", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300P (22 October 2010);

Back to Top