Learning Approaches for Remote Sensing Image Classification
Résumé
The latest generation of aerial- and satellite-based imaging sensors acquires huge volumes
of Earth’s images with high spatial, spectral and temporal resolution, which open the door
to a large range of important applications, such as the monitoring of natural disasters, the
planning of urban environments and precision agriculture. In order to fully exploit the
potential offered by these sensors, there is a need to develop accurate and time-efficient
mathematical models and algorithms for spectral-spatial analysis of the recorded high-
resolution data.
The main goal of my research is to develop learning approaches, which would help
to automatically interpret, or classify, remote sensing images. This manuscript presents
several strategies I have explored for this purpose, varying from the use of strong shape
priors to detect objects, regularization of classification probabilities on the image graphs,
and up to the use of convolutional neural network models capable to learn deep hierar-
chical contextual features.
The experimental results on diverse benchmarks of images and image time series show
the competitiveness of the developed methods when compared to the state-of-the-art ap-
proaches. In particular, we have recently created large-scale classification benchmark
of aerial images and have demonstrated that the modern deep learning-based methods
succeed in generalizing to the dissimilar urban settlements around the Earth. This opens
new exciting perspectives towards designing systems which would be able to automati-
cally update world-scale maps from remote sensing data.
Origine : Fichiers produits par l'(les) auteur(s)
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