Restoration and reconstruction of AVHRR images

Reichenbach, Stephen E.; Koehler, Daniel E.; Strelow, Dennis

doi:10.1109/36.406685

Cited by 30 publications

(15 citation statements)

References 12 publications

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“…The sensor optics, detector, electronic filters, and sensor motion are commonly modeled as a single acquisition PSF by convolving the individual PSF's of these effects together [33]. The optical image may then be modeled as the convolution of the scene radiance distribution with the acquisition PSF.…”

Section: A Satellite Imaging Modelmentioning

confidence: 99%

The impact of misregistration upon composited wide field of view satellite data and implications for change detection

Roy

2000

IEEE Trans. Geosci. Remote Sensing

111

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Composited wide field of view satellite data are used for many applications and increasingly for studies of global change. Although several compositing schemes have been suggested, all assume perfect geometric registration, which is not operationally feasible. In this study, models of the satellite imaging, geometric registration, and compositing processes are used to investigate the impact of misregistration upon the position of high contrast edges found in composited satellite data. Simulations are performed with respect to the compositing of advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data. Contrast edges are found to be systematically shifted in maximum and minimum value composites. The degree of shifting increases with the number of orbits that are composited, the degree of misregistration and the view zenith angle. The implications of these findings upon the utility of composited satellite data for change detection are discussed. The shifts may systematically bias estimates of location and area when composited data are used. They may also cause small and/or fragmented features, which are evident in individual orbits to disappear in composited data, precluding the ability to map such features or to detect their occurrence under a change detection scheme.

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Section: A Satellite Imaging Modelmentioning

confidence: 99%

The impact of misregistration upon composited wide field of view satellite data and implications for change detection

Roy

2000

IEEE Trans. Geosci. Remote Sensing

111

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“…Ideally, the 30 m degraded panchromatic reflectance should be derived by modelling the multispectral band point spread function (PSF) that describes the combined smoothing effects of the sensor detector electronics, optics, scanning mechanism, and atmospheric adjacency effects [41][42][43][44]. In this study, as the Landsat 8 OLI PSF for each band is unknown, a cubic-spline wavelet filter with a Gaussian-like frequency response followed by decimation was used to provide a first-order PSF approximation [45].…”

Section: Image Specific Spectral Weightsmentioning

confidence: 99%

Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening

Zhang

Roy

2016

Remote Sensing

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Pansharpening algorithms fuse higher spatial resolution panchromatic with lower spatial resolution multispectral imagery to create higher spatial resolution multispectral images. The free-availability and systematic global acquisition of Landsat 8 data indicate an expected need for global coverage and so computationally efficient Landsat 8 pansharpening. This study adapts and evaluates the established, and relatively computationally inexpensive, Brovey and context adaptive Gram Schmidt component substitution (CS) pansharpening methods for application to the Landsat 8 15 m panchromatic and 30 m red, green, blue, and near-infrared bands. The intensity images used by these CS pansharpening methods are derived as a weighted linear combination of the multispectral bands in three different ways using band spectral weights set (i) equally as the reciprocal of the number of bands; (ii) using fixed Landsat 8 spectral response function based (SRFB) weights derived considering laboratory spectra; and (iii) using image specific spectral weights derived by regression between the multispectral and the degraded panchromatic bands. The spatial and spectral distortion and computational cost of the different methods are assessed using Landsat 8 test images acquired over agricultural scenes in South Dakota, China, and India. The results of this study indicate that, for global Landsat 8 application, the context adaptive Gram Schmidt pansharpening with an intensity image defined using the SRFB spectral weights is appropriate. The context adaptive Gram Schmidt pansharpened results had lower distortion than the Brovey results and the least distortion was found using intensity images derived using the SRFB and image specific spectral weights but the computational cost using the image specific weights was greater than the using the SRFB weights. Recommendations for large area Landsat 8 pansharpening application are described briefly and the SRFB spectral weights are provided so users may implement computationally inexpensive Landsat 8 pansharpening themselves.

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“…More sophisticated reconstruction techniques, such as linear interpolation or cubic convolution, yield better results than nearest-neighbor interpolation, and techniques that restore as well as reconstruct can yield much better fidelity [1], [9], [10]. Our technique described in this paper not only reconstructs but also restores from overlapping images.…”

Section: Experimental Results For Optimal Constrained Filtersmentioning

confidence: 99%

Restoration and reconstruction from overlapping images for multi-image fusion

Reichenbach

2001

IEEE Trans. Geosci. Remote Sensing

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Abstract-This paper describes a technique for restoring and reconstructing a scene from overlapping images. In situations where there are multiple, overlapping images of the same scene, it may be desirable to create a single image that most closely approximates the scene, based on the data in all of the available images. For example, successive swaths acquired by NASA's moderate imaging spectrometer (MODIS) will overlap, particularly at wide scan angles, creating a severe visual artifact in the output image. Resampling the overlapping swaths to produce a more accurate image on a uniform grid requires restoration and reconstruction. The one-pass restoration and reconstruction technique developed in this paper yields mean-square optimal resampling, based on a comprehensive end-to-end system model that accounts for image overlap and is subject to user-defined and data-availability constraints on the spatial support of the filter.

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Restoration and reconstruction of AVHRR images

Cited by 30 publications

References 12 publications

The impact of misregistration upon composited wide field of view satellite data and implications for change detection

The impact of misregistration upon composited wide field of view satellite data and implications for change detection

Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening

Restoration and reconstruction from overlapping images for multi-image fusion

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