Dr. Kanchan Bahirat Profile

Dr. Kanchan Bahirat

at Simbe Robotics Inc

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Proceedings Article | 23 October 2010 Paper

A novel approach to land-cover maps updating in complex scenarios based on multitemporal remote sensing images

K. Bahirat, F. Bovolo, L. Bruzzone, S. Chaudhuri

Proceedings Volume 7830, 78300P (2010) https://doi.org/10.1117/12.866031

KEYWORDS: Image classification, Remote sensing, Statistical analysis, Distance measurement, Multispectral imaging, Expectation maximization algorithms, Image acquisition, Reliability, Machine learning, Actinium

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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.

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