Spatial and Spectral Quality Assessment of Fused Hyperspectral and Multispectral Data

Model based analysis or explicit definition/listing of all models/assumptions used in the derivation of a pan-sharpening method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods ‘better’ satisfying the needs of a particular application. Most existing pan-sharpening methods are based mainly on the two models/assumptions: spectral consistency for high resolution multispectral data (physical relationship between multispectral and panchromatic data in a high resolution scale) and spatial consistency for multispectral data (so-called Wald’s protocol first property or relationship between multispectral data in different resolution scales). Two methods, one based on a linear unmixing model and another one based on spatial unmixing, are described/proposed/modified which respect models assumed and thus can produce correct or physically justified fusion results. Earlier mentioned property ‘better’ should be measurable quantitatively, e.g. by means of so-called quality measures. The difficulty of a quality assessment task in multi-resolution image fusion or pan-sharpening is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions used are not valid or not fulfilled. From a model based view it follows naturally that a quality assessment measure can be defined as a combination of error model residuals using common or general models assumed in all fusion methods. Thus in this paper a comparison of the two earlier proposed/modified pan-sharpening methods is performed. Preliminary experiments based on visual analysis are carried out in the urban area of Munich city for optical remote sensing multispectral data and panchromatic imagery of the WorldView-2 satellite sensor.

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PAN-SHARPENING APPROACHES BASED ON UNMIXING OF MULTISPECTRAL REMOTE SENSING IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b7-693-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 693-702

Author(s):

G. Palubinskas

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Quality Assessment ◽

Visual Analysis ◽

Reference Image ◽

Optical Remote Sensing ◽

Physical Relationship ◽

Assessment Task ◽

Multispectral Data ◽

Model Based

Model based analysis or explicit definition/listing of all models/assumptions used in the derivation of a pan-sharpening method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods ‘better’ satisfying the needs of a particular application. Most existing pan-sharpening methods are based mainly on the two models/assumptions: spectral consistency for high resolution multispectral data (physical relationship between multispectral and panchromatic data in a high resolution scale) and spatial consistency for multispectral data (so-called Wald’s protocol first property or relationship between multispectral data in different resolution scales). Two methods, one based on a linear unmixing model and another one based on spatial unmixing, are described/proposed/modified which respect models assumed and thus can produce correct or physically justified fusion results. Earlier mentioned property ‘better’ should be measurable quantitatively, e.g. by means of so-called quality measures. The difficulty of a quality assessment task in multi-resolution image fusion or pan-sharpening is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions used are not valid or not fulfilled. From a model based view it follows naturally that a quality assessment measure can be defined as a combination of error model residuals using common or general models assumed in all fusion methods. Thus in this paper a comparison of the two earlier proposed/modified pan-sharpening methods is performed. Preliminary experiments based on visual analysis are carried out in the urban area of Munich city for optical remote sensing multispectral data and panchromatic imagery of the WorldView-2 satellite sensor.

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Quantitative spectral quality assessment technique validated using intraoperative in vivo Raman spectroscopy measurements

Journal of Biomedical Optics ◽

10.1117/1.jbo.25.4.040501 ◽

2020 ◽

Vol 25 (04) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Frédérick Dallaire ◽

Fabien Picot ◽

Jean-Philippe Tremblay ◽

Guillaume Sheehy ◽

Émile Lemoine ◽

...

Keyword(s):

Raman Spectroscopy ◽

Quality Assessment ◽

Spectral Quality ◽

Assessment Technique ◽

In Vivo Raman Spectroscopy

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Remote Sensing Image Quality Assessment Based on Engineering Quality and Spectral Quality

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1073-1076.1922 ◽

2014 ◽

Vol 1073-1076 ◽

pp. 1922-1933

Author(s):

Ying Li ◽

Can Cui ◽

Qi Gang Jiang ◽

Hong Ji Chen ◽

Xue Yuan Zhu

Keyword(s):

Remote Sensing ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Ecological Monitoring ◽

Remote Sensing Image ◽

Curve Space ◽

Spectral Quality ◽

Characteristic Space ◽

Data Source

This paper presented a new method to evaluate Remote Sensing image quality, by comparing ZY1-02C, ZY3, and SPOT5 images on the engineering quality and spectral quality. It is important to explore new options to evaluate different Remote Sensing image sources quality, in order to ensure the users could apply a best fit data source to environmental monitoring, ecological monitoring and so on. In this article, there were three aspects in the engineering quality assessment part, including the statistical character, the texture and the energy. And in the spectral quality assessment part, the imaging space, the curve space and the characteristic space were built to compare and measure different spectral ability of extracting ground objects among ZY1-02C, ZY3 and SPOT5 images. The result shows such a Remote Sensing image quality assessment can be generalized to choose suitable data source for some specific field.

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