A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images

Aiazzi, Bruno; Baronti, Stefano; Lotti, F.; Selva, Massimo

doi:10.1109/lgrs.2008.2012003

Cited by 177 publications

(103 citation statements)

References 13 publications

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“…Many methods have been proposed to realize the fusion process and they can be classified into two categories: component substitution CS-based and multiresolution analysis MRA-based techniques [50]. In order to test the capability of pansharpening for coastline extraction from Landsat-8 OLI imagery, ten widely used standard pansharpening algorithms encompassing different concept were chosen.…”

Section: Pansharpening Algorithmsmentioning

confidence: 99%

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

Liu

Wang

Ling

et al. 2017

Water

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Abstract:Coastline extraction is a fundamental work for coastal resource management, coastal environmental protection and coastal sustainable development. Due to the free access and long-term record, Landsat series images have the potential to be used for coastline extraction. However, dynamic features of different types of coastlines (e.g., rocky, sandy, artificial), caused by sea level fluctuation from tidal, storm and reclamation, make it difficult to be accurately extracted with coarse spatial resolution, e.g., 30 m, of Landsat images. To access this problem, we analyze the performance of coastline extraction by integrating downscaling, pansharpening and water index approaches in increasing the accuracy of coastline extraction from the latest Landsat-8 Operational Land Imager (OLI) imagery. In order to prove the availability of the proposed method, we designed three strategies: (1) Strategy 1 uses the traditional water index method to extract coastline directly from original 30 m Landsat-8 OLI multispectral (MS) image; (2) Strategy 2 extracts coastlines from 15 m fused MS images generated by integrating 15 m panchromatic (PAN) band and 30 m MS image with ten pansharpening algorithms; (3) Strategy 3 first downscales the PAN band to a finer spatial resolution (e.g., 7.5 m) band, and then extracts coastlines from pansharpened MS images generated by integrating downscaled spatial resolution PAN band and 30 m MS image with ten pansharpening algorithms. Using the coastline extracted from ZiYuan-3 (ZY-3) 5.8 m MS image as reference, accuracies of coastlines extracted from MS images in three strategies were validated visually and quantitatively. The results show that, compared with coastline extracted directly from 30 m Landsat-8 MS image (strategy 1), strategy 3 achieves the best accuracies with optimal mean net shoreline movement (MNSM) of −2.54 m and optimal mean absolute difference (MAD) of 11.26 m, followed by coastlines extracted in strategy 2 with optimal MNSM of −4.23 m and optimal MAD of 13.54 m. Further comparisons with single-band thresholding (Band 6), AWEI, and ISODATA also confirmed the superiority of strategy 3. For the various used pansharpening algorithms, five multiresolution analysis MRA-based pansharpening algorithms are more efficient than the component substitution CS-based pansharpening algorithms for coastline extraction from Landsat-8 OLI imagery. Among the five MRA-based fusion methods, the coastlines extracted from the fused images generated by Indusion, additive à trous wavelet transform (ATWT) and additive wavelet luminance proportional (AWLP) produced the most accurate and visually realistic representation. Therefore, pansharpening approaches can improve the accuracy of coastline extraction from Landsat-8 OLI imagery, and downscaling the PAN band to finer spatial resolution is able to further improve the coastline extraction accuracy, especially in crenulated coasts.

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Section: Pansharpening Algorithmsmentioning

confidence: 99%

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

Liu

Wang

Ling

et al. 2017

Water

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“…Figure 5 represents the workflow of block-based fusion using multispectral bands MS ℎ 1 and hyperspectral bands HS 1 . In the case of the pansharpening algorithm, we use the Gram-Schmidt (GS) method, which is a representative and efficient pansharpening algorithm [40]. In (1)-(4), the spatial resolution, the number of bands, and spectral wavelength of the synthetically fused images are equivalent to those of the original hyperspectral image.…”

Section: Generation Of Synthetically Fused Imagesmentioning

confidence: 99%

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

et al. 2017

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Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth's surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

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“…Thereafter, an optimal intensity image is generated based on multiple linear regression between multispectral bands and the degraded panchromatic image. Aiazzi et al (2009) have described this method in detail.…”

Section: Gsa (Gram-schmidt Adaptive) Algorithmmentioning

confidence: 99%

Performance Evaluation of Pansharpening Algorithms for WorldView-3 Satellite Imagery

Hyeok¹,

Park²,

Hee³

et al. 2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Worldview-3 satellite sensor provides panchromatic image with high-spatial resolution and 8-band multispectral images. Therefore, an image-sharpening technique, which sharpens the spatial resolution of multispectral images by using high-spatial resolution panchromatic images, is essential for various applications of Worldview-3 images based on image interpretation and processing. The existing pansharpening algorithms tend to tradeoff between spectral distortion and spatial enhancement. In this study, we applied six pansharpening algorithms to Worldview-3 satellite imagery and assessed the quality of pansharpened images qualitatively and quantitatively. We also analyzed the effects of time lag for each multispectral band during the pansharpening process. Quantitative assessment of pansharpened images was performed by comparing ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse), SAM (Spectral Angle Mapper), Q-index and sCC (spatial Correlation Coefficient) based on real data set. In experiment, quantitative results obtained by MRA (Multi-Resolution Analysis)-based algorithm were better than those by the CS (Component Substitution)-based algorithm. Nevertheless, qualitative quality of spectral information was similar to each other. In addition, images obtained by the CS-based algorithm and by division of two multispectral sensors were shaper in terms of spatial quality than those obtained by the other pansharpening algorithm. Therefore, there is a need to determine a pansharpening method for Worldview-3 images for application to remote sensing data, such as spectral and spatial information-based applications. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images

Cited by 177 publications

References 13 publications

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

Performance Evaluation of Pansharpening Algorithms for WorldView-3 Satellite Imagery

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