Balancing the Spatial and Spectral Quality of Satellite Fused Images through a Search Algorithm

Gonzalo-Martín, Consuelo; Lillo‐Saavedra, Mario

doi:10.5772/14452

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The objective is to obtain the fused image with the optimal combination of spectral characteristics preservation and spatial improvement. In this study, three metrics: peak signal-to-noise ratio (PSNR) [25], ERGAS [26] and The Mean Structure Similarity (MSSIM) index [27] have been used in order to determine the spatial and spectral quality of the MS fused images. Tables 3, 4 and 5 show a significantly higher spatial fidelity of QGA approach with regard to conv.…”

Section: Resultsmentioning

confidence: 99%

A Framework for Satellite Image Enhancement Using Quantum Genetic and Weighted IHS+Wavelet Fusion Method

Hamed¹,

Omer²,

Mohamed³

2016

ijacsa

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Abstract-this paper examined the applicability of quantum genetic algorithms to solve optimization problems posed by satellite image enhancement techniques, particularly superresolution, and fusion. We introduce a framework starting from reconstructing the higher-resolution panchromatic image by using the subpixel-shifts between a set of lower-resolution images (registration), then interpolation, restoration, till using the higher-resolution image in pan-sharpening a multispectral image by weighted IHS+Wavelet fusion technique. For successful superresolution, accurate image registration should be achieved by optimal estimation of subpixel-shifts. Optimal-parameters blind restoration and interpolation should be performed for the optimal quality higher-resolution image. There is a trade-off between spatial and spectral enhancement in image fusion; it is difficult for the existing methods to do the best in both aspects. The objective here is to achieve all combined requirements with optimal fusion weights, and use the parameters constraints to direct the optimization process. QGA is used to estimate the optimal parameters needed for each mathematic model in this framework "Super-resolution and fusion." The simulation results show that the QGA-based method can be used successfully to estimate automatically the approaching parameters which need the maximal accuracy, and achieve higher quality and efficient convergence rate more than the corresponding conventional GAbased and the classic computational methods.

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Section: Resultsmentioning

confidence: 99%

A Framework for Satellite Image Enhancement Using Quantum Genetic and Weighted IHS+Wavelet Fusion Method

Hamed¹,

Omer²,

Mohamed³

2016

ijacsa

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“…For the assessment of prediction accuracy at the spectral scale in a temporal domain among the STARFM, MQQA, and integrated MQQA-BME in the middle 14 days of the Landsat 8 repeat cycle, two more indicators, the Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS) [70] and the spectral angle mapper (SAM), are considered in addition to the R, STD, MAD, and RMSE. The ERGAS can be applied to assess the spectral similarity between the merged/fused data and the true values (i.e., the Landsat data set via manual removal up front before data merging/fusion) [70], [71] whereas the SAM can be used to retrieve the level of distortion of the merged/fused image [72]. The equations of R, STD, MAD, and RMSE are shown in Eqs.…”

Section: ) Quality Assessment Measuresmentioning

confidence: 99%

A Comparative Assessment of Multisensor Data Merging and Fusion Algorithms for High-Resolution Surface Reflectance Data

Wei

Chang

Bai

2020

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

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The improvement of the spatial and temporal resolution of reflectance data products has been challenging due to the diversity of data sources and availability of many data merging and fusion algorithms. In the algorithmic domain, methods for data merging and fusion may include, but are not limited to, the Modified Quantile-Quantile Adjustment (MQQA), the Bayesian maximum entropy (BME), and the spatial and temporal adaptive reflectance fusion model (STARFM). This paper presents a synergistic integration of the data merging and fusion algorithms of MQQA and BME in dealing with heterogeneous and nonstationary surface reflectance data at both the top of atmosphere (TOA) and land surface for a comparative study. Emphasis has been placed on the distinctive performance between BME and MQQA-BME algorithms in the spatial domain and the MQQA-BME and STARFM in the temporal domain at both TOA and land surface levels. The results indicate that the BME and MQQA-BME outperform the MQQA in terms of the spatial coverage at both TOA and land surface levels. Moreover, the MQQA-BME algorithm shows a higher prediction accuracy than STARFM at the blue band over the temporal domain at both TOA and land surface levels. The results of this comparison will greatly empower the MQQA-BME to be used for urban air quality monitoring and related epidemiological assessment in the future, once finer aerosol optical depth predictions via integrated data merging and fusion can be made possible.

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Determining the optimum image fusion method for better interpretation of the surface of the Earth

Yılmaz

Güngör

2016

Norsk Geografisk Tidsskrift - Norwegian Journal of Geography

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Balancing the Spatial and Spectral Quality of Satellite Fused Images through a Search Algorithm

Cited by 4 publications

References 29 publications

A Framework for Satellite Image Enhancement Using Quantum Genetic and Weighted IHS+Wavelet Fusion Method

A Framework for Satellite Image Enhancement Using Quantum Genetic and Weighted IHS+Wavelet Fusion Method

A Comparative Assessment of Multisensor Data Merging and Fusion Algorithms for High-Resolution Surface Reflectance Data

Determining the optimum image fusion method for better interpretation of the surface of the Earth

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