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. In particular a common CS solution is adopted for our reconstruction problem: the orthogonal matching pursuit (OMP) method. To illustrate the performances of the proposed method, a through experimental analysis on FORMOSAT-2 multispectral images is reported and discussed. It includes a simulation study and a comparison with a state-of-the-art technique for cloud removal. © 2012 IEEE.
Orthogonal Matching Pursuit for VHR Image Reconstruction
Lorenzi, Luca;Melgani, Farid;
2012-01-01
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. In particular a common CS solution is adopted for our reconstruction problem: the orthogonal matching pursuit (OMP) method. To illustrate the performances of the proposed method, a through experimental analysis on FORMOSAT-2 multispectral images is reported and discussed. It includes a simulation study and a comparison with a state-of-the-art technique for cloud removal. © 2012 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
