Deep learning methods for super-resolution of a remote sensing scene from multiple unregistered low-resolution images have recently gained attention thanks to a challenge proposed by the European Space Agency. This paper presents an evolution of the winner of the challenge, showing how incorporating non-local information in a convolutional neural network allows to exploit self-similar patterns that provide enhanced regularization of the super-resolution problem. Experiments on the dataset of the challenge show improved performance over the state-of-the-art, which does not exploit non-local information.

DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images / Molini, Andrea Bordone; Valsesia, Diego; Fracastoro, Giulia; Magli, Enrico. - (2020), pp. 3644-3656. ((Intervento presentato al convegno International Geoscience and Remote Sensing Symposium, IGARSS 2020 [10.1109/IGARSS39084.2020.9324418].

DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images

Molini, Andrea Bordone;Valsesia, Diego;Fracastoro, Giulia;Magli, Enrico
2020

Abstract

Deep learning methods for super-resolution of a remote sensing scene from multiple unregistered low-resolution images have recently gained attention thanks to a challenge proposed by the European Space Agency. This paper presents an evolution of the winner of the challenge, showing how incorporating non-local information in a convolutional neural network allows to exploit self-similar patterns that provide enhanced regularization of the super-resolution problem. Experiments on the dataset of the challenge show improved performance over the state-of-the-art, which does not exploit non-local information.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2844384