Superresolution of hyperspectral images using backpropagation neural networks

Mianji, Fereidoun A.; Zhang, Ye; Babakhani, Asad

doi:10.1109/inds.2009.5227984

Cited by 3 publications

(2 citation statements)

References 40 publications

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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%

Hyperspectral and Multispectral Image Fusion Using Cluster-Based Multi-Branch BP Neural Networks

Han

Luo

et al. 2019

Remote Sensing

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Fusion of the high-spatial-resolution hyperspectral (HHS) image using low-spatial- resolution hyperspectral (LHS) and high-spatial-resolution multispectral (HMS) image is usually formulated as a spatial super-resolution problem of LHS image with the help of an HMS image, and that may result in the loss of detailed structural information. Facing the above problem, the fusion of HMS with LHS image is formulated as a nonlinear spectral mapping from an HMS to HHS image with the help of an LHS image, and a novel cluster-based fusion method using multi-branch BP neural networks (named CF-BPNNs) is proposed, to ensure a more reasonable spectral mapping for each cluster. In the training stage, considering the intrinsic characteristics that the spectra are more similar within each cluster than that between clusters and so do the corresponding spectral mapping, an unsupervised clustering is used to divide the spectra of the down-sampled HMS image (marked as LMS) into several clusters according to spectral correlation. Then, the spectrum-pairs from the clustered LMS image and the corresponding LHS image are used to train multi-branch BP neural networks (BPNNs), to establish the nonlinear spectral mapping for each cluster. In the fusion stage, a supervised clustering is used to group the spectra of HMS image into the clusters determined during the training stage, and the final HHS image is reconstructed from the clustered HMS image using the trained multi-branch BPNNs accordingly. Comparison results with the related state-of-the-art methods demonstrate that our proposed method achieves a better fusion quality both in spatial and spectral domains.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral and Multispectral Image Fusion Using Cluster-Based Multi-Branch BP Neural Networks

Han

Luo

et al. 2019

Remote Sensing

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“…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%

Image fusion by spatially adaptive filtering using downscaling cokriging

Pardo‐Igúzquiza

Rodríguez-Galiano

Chica-Olmo

et al. 2011

ISPRS Journal of Photogrammetry and Remote Sensing

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Spectral Super-resolution for RGB Images using Class-based BP Neural Networks

Han

Xue

et al. 2018

2018 Digital Image Computing: Techniques and Applications (DICTA)

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Hyperspectral images are of high spectral resolution and have been widely used in many applications, but the imaging process to achieve high spectral resolution is at the expense of spatial resolution. This paper aims to construct a high-spatial-resolution hyperspectral (HHS) image from a highspatial-resolution RGB image, by proposing a novel class-based spectral super-resolution method. With the help of a set of RGB and HHS image-pairs, our proposed method learns nonlinear spectral mappings between RGB and HHS image-pairs using class-based back propagation neural networks (BPNNs). In the training stage, unsupervised clustering is used to divide an RGB image into several classes according to spectral correlation, and the spectrum-pairs from the classified RGB images and the corresponding HHS images are used to train the BPNNs, to establish the nonlinear spectral mapping for each class. In the spectral super-resolution stage, a supervised classification is used to classify the given RGB image into the classes determined during the training stage, and the final HHS image is reconstructed from the classified given RGB image using the trained BPNNs. Comparisons on three standard datasets, ICVL, CAVE and NUS, demonstrate that, our proposed method achieves a better spectral super-resolution quality than related state-of-the-art methods.

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Superresolution of hyperspectral images using backpropagation neural networks

Cited by 3 publications

References 40 publications

Hyperspectral and Multispectral Image Fusion Using Cluster-Based Multi-Branch BP Neural Networks

Hyperspectral and Multispectral Image Fusion Using Cluster-Based Multi-Branch BP Neural Networks

Image fusion by spatially adaptive filtering using downscaling cokriging

Spectral Super-resolution for RGB Images using Class-based BP Neural Networks

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