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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.
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.
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.
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