Reconstructing missing data in very high resolution (VHR) multispectral images represents a complex image processing challenge. In this paper, we propose a new method for the reconstruction of areas obscured by clouds. It is based on compressive sensing (CS) theory, which allows to find sparse signal representations in underdetermined linear equation systems. Here we propose a novel implementation which exploits genetic algorithms (GAs) and a new strategy for the selection of atoms belonging to the dictionary. To illustrate the performances of the proposed method, a thorough experimental analysis on FORMOSAT-2 images is reported and discussed. It includes a simulation study and a comparison with a state-of-the-art technique for cloud removal. © 2013 IEEE.
Contextual Genetic Algorithm for Compressive Sensing Reconstruction of VHR Images
Lorenzi, Luca;Melgani, Farid;
2013-01-01
Abstract
Reconstructing missing data in very high resolution (VHR) multispectral images represents a complex image processing challenge. In this paper, we propose a new method for the reconstruction of areas obscured by clouds. It is based on compressive sensing (CS) theory, which allows to find sparse signal representations in underdetermined linear equation systems. Here we propose a novel implementation which exploits genetic algorithms (GAs) and a new strategy for the selection of atoms belonging to the dictionary. To illustrate the performances of the proposed method, a thorough experimental analysis on FORMOSAT-2 images is reported and discussed. It includes a simulation study and a comparison with a state-of-the-art technique for cloud removal. © 2013 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
