Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image

Capobianco, Luca; Garzelli, Andrea; Nencini, F.; Alparone, Luciano; Baronti, Stefano

doi:10.1109/igarss.2007.4424023

Cited by 17 publications

(10 citation statements)

References 6 publications

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“…Different image fusion techniques take advantages of the complementary spatial/spectral resolution characteristics for producing the HR HS observation [8]- [12], [22]. We use to represent the LR HS data and to represent the HR PAN image, then the fusion process can be represented as where is the fusion rule and and represent the corresponding transformations.…”

Section: B Hs and Pan Image Sparse Fusionmentioning

confidence: 99%

Hyperspectral Imagery Super-Resolution by Spatial–Spectral Joint Nonlocal Similarity

Zhao

Yang

Chan

2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral (HS) super-resolution reconstruction is an ill-posed inversion problem, for which the solution from reconstruction constraint is not unique. To address this, an HS image super-resolution method is proposed to first utilize the joint regulation of spatial and spectral nonlocal similarities. We then fused the HS and panchromatic images with sparse regulation. With these two regulation terms, edge sharpness and spectrum consistency are preserved and noises are suppressed. The proposed method is tested with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperion images and evaluated by quantitative measures. The resulting enhanced images from the proposed method are superior to the results obtained by other well-known methods.

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Section: B Hs and Pan Image Sparse Fusionmentioning

confidence: 99%

Hyperspectral Imagery Super-Resolution by Spatial–Spectral Joint Nonlocal Similarity

Zhao

Yang

Chan

2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…With the growing availability and importance of HS images, researchers have begun considering both the fusion of PAN and HS images, i.e., hyperspectral pansharpening [22]- [24], and the fusion of MS and HS images, where the HS image is spatially enhanced based on the MS image [25]- [28]. This is a more difficult problem than the pansharpening problem, since we are not using a single PAN image but a multichannel MS image.…”

Section: Introductionmentioning

confidence: 97%

Model-Based Fusion of Multi- and Hyperspectral Images Using PCA and Wavelets

Pálsson

Sveinsson

Ulfarsson

et al. 2015

IEEE Trans. Geosci. Remote Sensing

159

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In remote sensing, due to cost and complexity issues, multispectral (MS) and hyperspectral (HS) sensors have significantly lower spatial resolution than panchromatic (PAN) images. Recently, the problem of fusing coregistered MS and HS images has gained some attention. In this paper, we propose a novel method for fusion of MS/HS and PAN images and of MS and HS images. MS and, more so, HS images contain spectral redundancy, which makes the dimensionality reduction of the data via principal component (PC) analysis very effective. The fusion is performed in the lower dimensional PC subspace; thus, we only need to estimate the first few PCs, instead of every spectral reflectance band, and without compromising the spectral and spatial quality. The benefits of the approach are substantially lower computational requirements and very high tolerance to noise in the observed data. Examples are presented using WorldView 2 data and a simulated data set based on a real HS image, with and without added noise.Index Terms-Image fusion, maximum a posteriori probability (MAP), principal component analysis (PCA), wavelets.

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“…The spatial resolution can be enhanced by fusing the multiband polarization image with the high spatial resolution broadband panchromatic image. A variety of techniques have been proposed in the literature for combining imagery of different spatial and spectral resolutions [9], such as component substitution methods and high-pass techniques [9][10][11]. Component substitution methods transform the multispectral imagery and replace one component with the broadband high-resolution imagery.…”

Section: Introductionmentioning

confidence: 99%

“…High-pass techniques inject high spatial frequency content from the highresolution image to the bands of the lower-resolution data. Some other approaches to generating a higher resolution image normally require as input multiple low-resolution images of the same scene, which are aligned with subpixel accuracy [10].…”

Section: Introductionmentioning

confidence: 99%

High-resolution multiband polarization epithelial tissue imaging method by sparse representation and fusion

2012

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Multiband polarization epithelial tissue imaging is an effective tool to measure tissue's birefringence and structure for quantitative pathology analysis. To discriminate the pathology accurately, high-resolution multiband polarization images are essential. But it is difficult to acquire high-resolution polarization images because of the limitations of imaging systems. The polarization image calculation process can be regarded as image fusion with fixed rules, and multiband polarization images are intrinsically sparse. In this paper, we propose a novel high-resolution multiband polarization image calculation method by utilizing the sparse representation and image fusion method. The multiband images are first represented in the sparse domain and we further introduce total-variation-regularization terms into the sparse representation framework. Then, polarization parameter images are calculated by simultaneous fusion and reconstruction. Higher quality multiband polarization images can be obtained through additional regularization constraint in the fusion process. Extensive experiments validate that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both peak signal-to-noise-ratio and visual perception.

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Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image

Cited by 17 publications

References 6 publications

Hyperspectral Imagery Super-Resolution by Spatial–Spectral Joint Nonlocal Similarity

Hyperspectral Imagery Super-Resolution by Spatial–Spectral Joint Nonlocal Similarity

Model-Based Fusion of Multi- and Hyperspectral Images Using PCA and Wavelets

High-resolution multiband polarization epithelial tissue imaging method by sparse representation and fusion

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