Search Results for author:
Found 7 papers, 0 papers with code
Deep priors for satellite image restoration with accurate uncertainties
In particular, VBLE-xz is a scalable method to get realistic posterior samples and accurate uncertainties, while SatDPIR represents a compelling alternative to direct inversion methods when uncertainty quantification is not required.
Variational Bayes image restoration with compressive autoencoders
Regularization of inverse problems is of paramount importance in computational imaging.
CD-GAN: a robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors
In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e. g., optical or radar).
Coupled dictionary learning for unsupervised change detection between multi-sensor remote sensing images
This paper addresses the problem of unsupervisedly detecting changes between two observed images acquired by sensors of different modalities with possibly different resolutions.
Robust fusion algorithms for unsupervised change detection between multi-band optical images - A comprehensive case study
Unsupervised change detection techniques are generally constrained to two multi-band optical images acquired at different times through sensors sharing the same spatial and spectral resolution.
Robust Fusion of Multi-Band Images with Different Spatial and Spectral Resolutions for Change Detection
To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e. g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images.
Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: a Fusion-Based Approach
Change detection is one of the most challenging issues when analyzing remotely sensed images.
