Advances in Hyperspectral and Multispectral Image Fusion and Spectral Unmixing

Lanaras, Charis; Baltsavias, E.; Schindler, Konrad

doi:10.5194/isprsarchives-xl-3-w3-451-2015

Cited by 16 publications

(8 citation statements)

References 29 publications

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“…Merging of MS and HS images into a single, enhanced image is sometimes termed as hyperspectral image fusion or hyperspectral super-resolution (Lanaras et al 2015a). Hyperspectral image fusion denotes the problem of combining a HS image with another image of higher spatial resolution MS image to spatially localize the abundances.…”

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confidence: 99%

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Mookambiga

Gomathi²

2016

Multidim Syst Sign Process

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The volume of data grows with the advent of multiple types of remote sensing sensors and in order to extract the most useful information there is a need to combine the data gathered from the different sources. The widely used panchromatic and multispectral imageries in many applications offer decimetric and metric spatial resolution. However, the spectral resolution of these images is poor. Hyperspectral imaging has unique characteristics of providing very fine spectral resolution in a large number of bands with decametric spatial resolution and found to be highly useful for a wide span of application areas that requires high spectral resolution. The fusion of spectral and spatial information provides an effective way of enhancing the spatial quality of hyperspectral imagery as well as a method for preserving spectral quality. This fusion process is not a trivial task as always there has been a tradeoff between the preservation of spatial and spectral quality information as in the original sources of fusion. In this paper, a review on hyperspectral pansharpening and hyperspectral multispectral fusion based approaches has been reported. The widely adopted quantitative and qualitative performance measures to verify the fusion results are highlighted. In addition, the challenges in existing fusion techniques have also been discussed.

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confidence: 99%

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Mookambiga

Gomathi²

2016

Multidim Syst Sign Process

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“…Huang et al [29] presented a novel spatial and spectral fusion model (SASFM) that uses sparse matrix factorization to fuse remote sensing imagery with different spatial and spectral properties. Lanaras et al [30] jointly processed high spectral and high geometric resolution images and exploited their synergies to generate a fused image of high spectral and geometric resolution, and improve (linear) spectral unmixing of hyperspectral endmembers at subpixel level, which is the pixel size of the hyperspectral image. Dian et al [31] proposed a novel hyperspectral image (HIS) super-resolution method based on non-local sparse tensor factorization (called as the NLSTF).…”

Section: B Representation Models For Multispectral Imagesmentioning

confidence: 99%

Multivector Sparse Representation for Multispectral Images Using Geometric Algebra

2019

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Multispectral images can provide more faithful representations for real scenes than the traditional images and improve the performance of image restoration tasks. In this paper, we propose a novel multivector sparse representation model for multispectral images using geometric algebra (GA), with the truth that GA is now well used in image processing and it gives a formidable way to represent multispectral images. The proposed model represents a multispectral image as a GA multivector by fully considering the spatial and spectral information, where a GA dictionary learning algorithm is presented using the K-GA-singular value decomposition (GASVD) (generalized K-means clustering for GASVD) method. Consequently, with the complete consideration of the relationship between spectral channels in multispectral images, artifacts and blurring effects can be successfully avoided. The experimental results demonstrate that the proposed sparse model surpasses the existing methods for multispectral images reconstruction and denoising tasks by capturing correlations between spectral channels thoroughly and shows its usefulness and effectiveness for multispectral images processing.INDEX TERMS Multivector, sparse representation, geometric algebra (GA), multispectral images, dictionary learning, K-GASVD.

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“…Zhang et al [28] proposed an example-based super-resolution mapping model by using the support vector regression to generate fine resolution maps. An integrated process was performed in [29] to jointly solve the image fusion and spectral unmixing problems. A subpixel resolution thematic map framework is presented in [30], while a collaborative representation-based SPM method [31] was proposed to generate improved classification maps at the subpixel scale.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution Inspired by Deep Laplacian Pyramid Network

He¹,

Liu

2018

Remote Sensing

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Existing hyperspectral sensors usually produce high-spectral-resolution but low-spatial-resolution images, and super-resolution has yielded impressive results in improving the resolution of the hyperspectral images (HSIs). However, most of the super-resolution methods require multiple observations of the same scene and improve the spatial resolution without fully considering the spectral information. In this paper, we propose an HSI super-resolution method inspired by the deep Laplacian pyramid network (LPN). First, the spatial resolution is enhanced by an LPN, which can exploit the knowledge from natural images without using any auxiliary observations. The LPN progressively reconstructs the high-spatial-resolution images in a coarse-to-fine fashion by using multiple pyramid levels. Second, spectral characteristics between the low- and high-resolution HSIs are studied by the non-negative dictionary learning (NDL), which is proposed to learn the common dictionary with non-negative constraints. The super-resolution results can finally be obtained by multiplying the learned dictionary and its corresponding sparse codes. Experimental results on three hyperspectral datasets demonstrate the feasibility of the proposed method in enhancing the spatial resolution of the HSI with preserving the spectral information simultaneously.

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Advances in Hyperspectral and Multispectral Image Fusion and Spectral Unmixing

Cited by 16 publications

References 29 publications

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery

Multivector Sparse Representation for Multispectral Images Using Geometric Algebra

Hyperspectral Image Super-Resolution Inspired by Deep Laplacian Pyramid Network

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