2009 2nd International Workshop on Nonlinear Dynamics and Synchronization 2009
DOI: 10.1109/inds.2009.5227984
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Superresolution of hyperspectral images using backpropagation neural networks

Abstract: Hyperspectral technology has introduced a new perspective in remote sensing applications but suffers from low spatial resolution. A new spatial-spectral data fusion technique based on spectral mixture analysis and super-resolution mapping for spatial resolution enhancement of hyperspectral imagery is proposed in this paper. To this end a linear mixture model and a fully constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional … Show more

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Cited by 3 publications
(2 citation statements)
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“…Very recently, neural network based methods have been widely used in many different areas with much more better performance, such as super-resolution [17], target detection [18], and have also been used in image fusion tasks. Using deep CNN with two branches, Yang et al [19] proposed a fusion method to extract features from LHS and HMS images, respectively.…”
Section: Introductionmentioning
confidence: 99%
“…Very recently, neural network based methods have been widely used in many different areas with much more better performance, such as super-resolution [17], target detection [18], and have also been used in image fusion tasks. Using deep CNN with two branches, Yang et al [19] proposed a fusion method to extract features from LHS and HMS images, respectively.…”
Section: Introductionmentioning
confidence: 99%
“…Several methods have been proposed for image fusion including the intensity-hue-saturation (IHS) method (Choi, 2006;Tu et al, 2005), principal components analysis (PCA) (Ehlers, 1991;RigolSánchez and Chica-Olmo, 1998), high-pass filter (HPF) method (Chavez et al, 1991), smoothing filter based intensity modulation (Liu, 2000), wavelet transform and multiscale Kalman filter (Aiazzi et al, 2002;Amolins et al, 2007;Garguet-Duport et al, 1996;LilloSaavedra and Gonzalo, 2006;Nuñez et al, 1999;Ranchin and Wald, 2000;Ranchin et al, 2003;Simone et al, 2002) and machine learning methods (Mianji et al, 2009;Zheng et al, 2008) to name just a few. It is beyond the scope of this paper to review and compare the above methods, among which is cokriging, and the geostatistical multivariate prediction method (Memarsadeghi et al, 2005;Nishii et al, 1996).…”
Section: Introductionmentioning
confidence: 99%