Basic Spatial Resolution Metrics for Satellite Imagers

Valenzuela, Alvaro; Reyes, Juan Carlos González

doi:10.1109/jsen.2019.2902512

Cited by 28 publications

(25 citation statements)

References 17 publications

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“…It has been noted that the NIIRS rating of submetric satellite raw images strongly correlates with their in orbit spatial resolution measurements (Valenzuela, 2019). The relationship between GIQE 5 and spatial resolution is a matter of future research.…”

Section: Discussionmentioning

confidence: 96%

Comparative Study of the Different Versions of the General Image Quality Equation

Valenzuela

Reyes

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The General Image Quality Equation (GIQE) is an analytical tool derived by regression modelling that is routinely employed to gauge the interpretability of raw and processed images, computing the most popular quantitative metric to evaluate image quality; the National Image Interpretability Rating Scale (NIIRS). There are three known versions of this equation; GIQE&nbsp;3, GIQE&nbsp;4 and GIQE&nbsp;5, but the last one is scarcely known. The variety of versions, their subtleties, discontinuities and incongruences, generate confusion and problems among users. The first objective of this paper is to identify typical sources of confusion in the use of the GIQE, suggesting novel solutions to the main problems found in its application and presenting the derivation of a continuous form of GIQE&nbsp;4, denominated GIQE&nbsp;4C, that provides better correlation with GIQE&nbsp;3 and GIQE&nbsp;5. The second objective of this paper is to compare the predictions of GIQE&nbsp;4C and GIQE&nbsp;5, regarding the maximum image quality rating that can be achieved by image processing techniques. It is concluded that the transition from GIQE&nbsp;4 to GIQE&nbsp;5 is a major paradigm shift in image quality metrics, because it reduces the benefit of image processing techniques and enhances the importance of the raw image and its signal to noise ratio.</p>

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Section: Discussionmentioning

confidence: 96%

Comparative Study of the Different Versions of the General Image Quality Equation

Valenzuela

Reyes

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Thus, the choice of the most suitable metric for representing and measuring this feature is not straightforward as it appears on a first instance [6,7]. Within the scientific community, this topic has been an open question for decades (e.g., [8]), and nowadays, it is still an active field of research (e.g., [9]).…”

Section: Introductionmentioning

confidence: 99%

Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

Cenci¹,

Pampanoni

Laneve

et al. 2021

Remote Sensing

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Developing reliable methodologies of data quality assessment is of paramount importance for maximizing the exploitation of Earth observation (EO) products. Among the different factors influencing EO optical image quality, sharpness has a relevant role. When implementing on-orbit approaches of sharpness assessment, such as the edge method, a crucial step that strongly affects the final results is the selection of suitable edges to use for the analysis. Within this context, this paper aims at proposing a semi-automatic, statistically-based edge method (SaSbEM) that exploits edges extracted from natural targets easily and largely available on Earth: agricultural fields. For each image that is analyzed, SaSbEM detects numerous suitable edges (e.g., dozens-hundreds) characterized by specific geometrical and statistical criteria. This guarantees the repeatability and reliability of the analysis. Then, it implements a standard edge method to assess the sharpness level of each edge. Finally, it performs a statistical analysis of the results to have a robust characterization of the image sharpness level and its uncertainty. The method was validated by using Landsat 8 L1T products. Results proved that: SaSbEM is capable of performing a reliable and repeatable sharpness assessment; Landsat 8 L1T data are characterized by very good sharpness performance.

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“…The value for MTF at 10% modulation contrast often is referred to as resolution limit or cut-off frequency of MTF H(k)= 0.10 at spatial frequency k MT F10 where its reciprocal H(r)(PSF) corresponds to the least resolved scale in image domain. This scale factor multiplied by nominal ground sample distance (GSD) then delivers the least resolved distance and is named ground resolved distance (GRD) [19][20][21][22].…”

Section: Image Quality and Spatial Resolutionmentioning

confidence: 99%

Implementation and Validation of a High Accuracy UAV-Photogrammetry Based Rail Track Inspection System

et al. 2021

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The regular inspection of the crane tracks of storage cranes at the Container Terminal Altenwerder (CTA), Hamburg requires high accuracy of measurements to determine its position. The allowed tolerances are in the range of 10 mm in the XY plane on a track length of 300 m. The traditional semi-automatic surveying methods are slow and require the interruption of the activities in the storage blocks. The research project AeroInspekt proposed a fully automatic measurement of the position of the tracks using UAV-based photogrammetry. In this paper, the results of the test campaign, carried out in June 2020, were presented where different cameras (150 mm and 80 mm telelens) and flight speeds (1.1 m/s and 1.9 m/s) at a 35 m flying height were performed. Furthermore, an automated rail delineation in the derived surface model was developed and evaluated with ground reference measurements. The results show that the required accuracy of the rail position with an RMSE of 3 mm in XY plane and 8 mm in altitude can be achieved with comparatively less disruption of regular block activities.

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Basic Spatial Resolution Metrics for Satellite Imagers

Cited by 28 publications

References 17 publications

Comparative Study of the Different Versions of the General Image Quality Equation

Comparative Study of the Different Versions of the General Image Quality Equation

Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

Implementation and Validation of a High Accuracy UAV-Photogrammetry Based Rail Track Inspection System

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