@inproceedings{1badbb2d6c5943f8b4449c020908af69,
title = "Multi-sensor change detection based on nonlinear canonical correlations",
abstract = "The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of such datasets. However, for change detection existing methods are often unable to deal with heterogeneous image sources and very few consider possible nonlinearities in the data. Additionally, the availability of labeled information is very limited in change detection applications. For these reasons, we present the use of a semi-supervised kernel-based feature extraction technique. It incorporates a manifold regularization accounting for the geometric distribution and jointly addressing the small sample problem. An exhaustive example using Landsat 5 data illustrates the potential of the method for multi-sensor change detection.",
keywords = "Change detection, Feature extraction, Multi-sensor, Multimodal, Radiometric normalization",
author = "Michele Volpi and {De Morsier}, Frank and Gustavo Camps-Valls and Mikhail Kanevski and Devis Tuia",
year = "2013",
month = dec,
doi = "10.1109/IGARSS.2013.6723187",
language = "English",
isbn = "9781479911141",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
pages = "1944--1947",
booktitle = "2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings",
note = "2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 ; Conference date: 21-07-2013 Through 26-07-2013",
}