6 research outputs found
Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling
L'abstract è presente nell'allegato / the abstract is in the attachmen
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
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.Comment: arXiv admin note: text overlap with arXiv:1907.0649
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
SAR despeckling is a problem of paramount importance in remote sensing, since it represents the first step of many scene analysis algorithms. Recently, deep learning techniques have outperformed classical model-based despeckling algorithms. However, such methods require clean ground truth images for training, thus resorting to synthetically speckled optical images since clean SAR images cannot be acquired. In this paper, inspired by recent works on blind-spot denoising networks, we propose a self-supervised Bayesian despeckling method. The proposed method is trained employing only noisy images and can therefore learn features of real SAR images rather than synthetic data. We show that the performance of the proposed network is very close to the supervised training approach on synthetic data and competitive on real data