Hypercomplex Quality Assessment of Multi/Hyperspectral Images

Garzelli, Andrea; Nencini, F.

doi:10.1109/lgrs.2009.2022650

Cited by 284 publications

(105 citation statements)

References 12 publications

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“…Quantitative comparison of the data was undertaken using the Q2 n metric that quantifies the average spatial and spectral distortion between any number of bands between two images [53]. The Q2 n metric is a single value bounded between 0 and 1 (where higher values indicate greater quality and less distortion) and is an extension of the single band universal image quality index (UQI) [54].…”

Section: Evaluation Methodologymentioning

confidence: 99%

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Zhang

Roy

et al. 2017

Remote Sensing

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Abstract:The Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) method to downscale Landsat-8 Operational Land Imager (OLI) 30-m data to Sentinel-2 multi-spectral instrument (MSI) 20-m resolution is presented. The method first downscales the Landsat-8 30-m OLI bands to 15-m using the spatial detail provided by the Landsat-8 15-m panchromatic band and then reprojects and resamples the downscaled 15-m data into registration with Sentinel-2A 20-m data. The LPAD method is demonstrated using pairs of contemporaneous Landsat-8 OLI and Sentinel-2A MSI images sensed less than 19 min apart over diverse geographic environments. The LPAD method is shown to introduce less spectral and spatial distortion and to provide visually more coherent data than conventional bilinear and cubic convolution resampled 20-m Landsat OLI data. In addition, results for a pair of Landsat-8 and Sentinel-2A images sensed one day apart suggest that image fusion should be undertaken with caution when the images are acquired under different atmospheric conditions. The LPAD source code is available at GitHub for public use.

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Section: Evaluation Methodologymentioning

confidence: 99%

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Zhang

Roy

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The first procedure compared the fused imagery generated from the degraded MS and PAN images of the two sensors with the corresponding original MS images according to Wald's protocol [47]. Three comprehensive indices, the Erreur Relative Globale Adimensionnell de Synthèse (ERGAS) [48], the Spectral Angle Mapper (SAM) [49,50] and Q2 n [51,52], which is a generalization of the Universal Image Quality Index (UIQI) for monoband images and derived from the theory of hypercomplex numbers, particularly of 2 n -ones, were employed to do this. These indices estimate the global spectral quality of the fused images.…”

Section: Test Data Fusion Methods For Comparison and Evaluation Critmentioning

confidence: 99%

Improved Pansharpening with Un-Mixing of Mixed MS Sub-Pixels near Boundaries between Vegetation and Non-Vegetation Objects

Jing

Wei

et al. 2016

Remote Sensing

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Abstract:Pansharpening is an important technique that produces high spatial resolution multispectral (MS) images by fusing low spatial resolution MS images and high spatial resolution panchromatic (PAN) images of the same area. Although numerous successful image fusion algorithms have been proposed in the last few decades to reduce the spectral distortions in fused images, few of these take into account the spectral distortions caused by mixed MS sub-pixels (MSPs). Typically, the fused versions of MSPs remain mixed, although some of the MSPs correspond to pure PAN pixels. Due to the significant spectral differences between vegetation and non-vegetation (VNV) objects, the fused versions of MSPs near VNV boundaries cause blurred VNV boundaries and significant spectral distortions in the fused images. In order to reduce the spectral distortions, an improved version of the haze-and ratio-based fusion method is proposed to realize the spectral un-mixing of MSPs near VNV boundaries. In this method, the MSPs near VNV boundaries are identified first. The identified MSPs are then defined as either pure vegetation or non-vegetation pixels according to the categories of the corresponding PAN pixels. Experiments on WorldView-2 and IKONOS images of urban areas using the proposed method yielded fused images with significantly clearer VNV boundaries and smaller spectral distortions than several other currently-used image fusion methods.

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“…As a final quality metric we use Q2 n , an extension of the Universal Image Quality Index (UQI) from single-band to multispectral/hyperspectral images, based on hypercomplex numbers [37]. The Q2 n takes on values between −1 (worst) and 1 (best).…”

Section: Error Metrics and Baselinesmentioning

confidence: 99%

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

2017

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Hyperspectral sensors capture a portion of the visible and near-infrared spectrum with many narrow spectral bands. This makes it possible to better discriminate objects based on their reflectance spectra and to derive more detailed object properties. For technical reasons, the high spectral resolution comes at the cost of lower spatial resolution. To mitigate that problem, one may combine such images with conventional multispectral images of higher spatial, but lower spectral resolution. The process of fusing the two types of imagery into a product with both high spatial and spectral resolution is called hyperspectral super-resolution. We propose a method that performs hyperspectral super-resolution by jointly unmixing the two input images into pure reflectance spectra of the observed materials, along with the associated mixing coefficients. Joint super-resolution and unmixing is solved by a coupled matrix factorization, taking into account several useful physical constraints. The formulation also includes adaptive spatial regularization to exploit local geometric information from the multispectral image. Moreover, we estimate the relative spatial and spectral responses of the two sensors from the data. That information is required for the super-resolution, but often at most approximately known for real-world images. In experiments with five public datasets, we show that the proposed approach delivers up to 15% improved hyperspectral super-resolution.

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Hypercomplex Quality Assessment of Multi/Hyperspectral Images

Cited by 284 publications

References 12 publications

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Improved Pansharpening with Un-Mixing of Mixed MS Sub-Pixels near Boundaries between Vegetation and Non-Vegetation Objects

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

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