Weakly Supervised Alignment Of Image Manifolds With Semantic Ties

Aligning data distributions that underwent spectral distortions related to acquisition conditions is a key issue to improve the performance of classifiers applied to multi-temporal and multi-angular images. In this paper, we propose a feature extraction methodology, which aligns data manifolds based on their internal geometric structure and on a series of object correspondences highlighted on each image, or tie points. The weakly supervised manifold alignment (WeSMA) is a feature extractor that allows to define a common latent space, in which the images can be projected and processed by the same classifier. WeSMA relaxes the need for labeled pixels in all acquisitions of previous manifold alignment methods, an heavy constraint for remote sensing applications. Experiments on a set of World-View II images acquired at different viewing angles show the interest of the method that can compensate the spectral shift generated by the angular distortion without labels issued from the off-nadir image.


Published in:
2014 Ieee International Geoscience And Remote Sensing Symposium (Igarss), 3546-3549
Presented at:
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec, CANADA, JUL 13-18, 2014
Year:
2014
Publisher:
New York, Ieee
ISSN:
2153-6996
ISBN:
978-1-4799-5775-0
Laboratories:




 Record created 2015-04-13, last modified 2018-03-13


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