An object-level strategy for pan-sharpening quality assessment of high-resolution satellite imagery

Dadrasjavan, F.; Samadzadegan, Farhad

doi:10.1016/j.asr.2014.08.024

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…The quality of the pansharpened image was inconsistent among the different regions and different land covers; thus, most of the quantitative quality assessment strategies that involve assigning a single value to the whole image could not efficiently evaluate the effectiveness of the pansharpening methods [44]. An object-level or land-cover-based pansharpening quality assessment strategy could overcome the limitations of the traditional quantitative quality assessment strategies, evaluate the performance of the pansharpening process for different land covers, and quantify the quality of different land covers, thus improving our understanding of the influence of pansharpening methods across different land covers [15,44]. However, relevant research has only focused on a few types of land covers [15,44], and it is necessary to evaluate the performance of the pansharpening process for more land covers.…”

Section: Discussionmentioning

confidence: 99%

“…An object-level or land-cover-based pansharpening quality assessment strategy could overcome the limitations of the traditional quantitative quality assessment strategies, evaluate the performance of the pansharpening process for different land covers, and quantify the quality of different land covers, thus improving our understanding of the influence of pansharpening methods across different land covers [15,44]. However, relevant research has only focused on a few types of land covers [15,44], and it is necessary to evaluate the performance of the pansharpening process for more land covers. The objective of the present study was to elucidate the effects of commonly used pansharpening methods when they are applied to GF-2 imagery by performing quality assessments based on the region and land cover.…”

Section: Discussionmentioning

confidence: 99%

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Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery

Liu

Huang

2020

Applied Sciences

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The existing pansharpening methods applied to recently obtained satellite data can produce spectral distortion. Therefore, quality assessments should be performed to address this. However, quality assessment of the whole image may not be sufficient, because major differences in a given region or land cover can be minimized by small differences in another region or land cover in the image. Thus, it is necessary to evaluate the performance of the pansharpening process for different regions and land covers. In this study, the widely used modified intensity-hue-saturation (mIHS), Gram–Schmidt spectral sharpening (GS), color spectral sharpening (CN), and principal component analysis (PCA) pansharpening methods were applied to Gaofen 2 (GF-2) imagery and evaluated according to region and land-cover type, which was determined via an object-oriented image analysis technique with a support vector machine-supervised method based on several reliable quality indices at the native spatial scale without reference. Both visual and quantitative analyses based on region and land cover indicated that all four approaches satisfied the demands for improving the spatial resolution of the original GF-2 multispectral (MS) image, and mIHS produced results superior to those of the GS, CN, and PC methods by preserving image colors. The results indicated differences in the pansharpening quality among different land covers. Generally, for most land-cover types, the mIHS method better preserved the spectral information and spatial autocorrelation compared with the other methods.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery

Liu

Huang

2020

Applied Sciences

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“…As an initial study, DadrasJavan and Samadzadegan (2014) have proposed an objectlevel strategy for the quality assessment of pan-sharpening products. Then, Dehnavi and Mohammadzadeh (2015) performed a class-based fusion to find out the best parameters of some fusion methods which are suitable for particular land covers.…”

Section: Conventional Strategies For Quality Assessment Of Pan-sharpe...mentioning

confidence: 99%

“…Contrary to the similar studies that set the image segments as their image objects for their object-level IQA calculations (i.e. DadrasJavan and Samadzadegan 2014, Restaino et al 2016, Rodríguez-Esparragón et al 2017, we arranged the image classes (derived from classification) as the objects for the object-level SQA to understand semantic relations between specific land covers and fusion functions. In the following, the procedure of image object extraction and object-level SQA is explained.…”

Section: Problem Descriptionmentioning

confidence: 99%

Varying weighted spatial quality assessment for high resolution satellite image pan-sharpening

Mehravar

Javan

Samadzadegan

et al. 2021

International Journal of Image and Data Fusion

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This paper focuses on spatial quality assessment of pan-sharpened imagery that contains valuable information of input images. Its aim is to show that fusion functions respond differently to different types of landscapes. It compares a quality assessment of an objectlevel procedure with that of a conventional pixel-level-based procedure which assigns uniform quality scores to all image pixels of pan-sharpened images. To do so, after performing a series of pansharpening evaluations, a weighted procedure for spatial quality assessments of pan-sharpening products, allocating spatially varying weight factors to the image pixels proportional to their level of spatial information content is proposed. All experiments are performed using five high-resolution image datasets using fusion products produced by three common pan-sharpening algorithms. The datasets are acquired from WorldView-2, QuickBird, and IKONOS. Experimental results show that the spatial distortion of fused images for the class vegetation cover exceeds that of man-made structures, reaching more than 4% in some cases. Our procedure can preclude illogical fidelity estimations occurring when pansharpened images contain different land covers. Since particular image structures are of high importance in remote sensing applications, our procedure provides a purpose-oriented estimation of the spatial quality for pan-sharpened images in comparison with conventional procedures.

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“…Application of either MS or PAN images alone means that parts of information content are discarded. Image fusion techniques are frequently used to combine two or more images to produce enhanced images (Pohl and Van Genderen, 1998;DadrasJavan and Samadzadegan, 2014;Alidoost et al, 2015;Jagalingam and Hegde, 2015). When a fusion method is used to fuse MS and PAN images of the same scene acquired by the same satellite, the fusion task is called "pan-sharpening".…”

Section: Introductionmentioning

confidence: 99%

A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery

Javan

Samadzadegan

Mehravar

et al. 2021

ISPRS Journal of Photogrammetry and Remote Sensing

116

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An object-level strategy for pan-sharpening quality assessment of high-resolution satellite imagery

Cited by 10 publications

References 15 publications

Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery

Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery

Varying weighted spatial quality assessment for high resolution satellite image pan-sharpening

A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery

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