We propose in this work new strategies to reconstruct areas obscured by opaque clouds in multispectral images. They are based on an autoencoder (AE) neural network which opportunely models the relationships between a given source (cloud-free) image and a target (cloud-contaminated) image. The first strategy estimates the relationship model at a pixel level while the second one operates at a patch level in order profit from spatial contextual information. Experimental results obtained on FORMOSAT-2 images are reported and discussed together with a comparison with reference techniques.
Autoencoding approach to the cloud removal problem
Salim Malek;
2017-01-01
Abstract
We propose in this work new strategies to reconstruct areas obscured by opaque clouds in multispectral images. They are based on an autoencoder (AE) neural network which opportunely models the relationships between a given source (cloud-free) image and a target (cloud-contaminated) image. The first strategy estimates the relationship model at a pixel level while the second one operates at a patch level in order profit from spatial contextual information. Experimental results obtained on FORMOSAT-2 images are reported and discussed together with a comparison with reference techniques.File in questo prodotto:
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