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.2009;9(5):3289-313.
doi: 10.3390/s90503289. Epub 2009 May 5.

A procedure for high resolution satellite imagery quality assessment

Affiliations

A procedure for high resolution satellite imagery quality assessment

Mattia Crespi et al. Sensors (Basel).2009.

Abstract

Data products generated from High Resolution Satellite Imagery (HRSI) are routinely evaluated during the so-called in-orbit test period, in order to verify if their quality fits the desired features and, if necessary, to obtain the image correction parameters to be used at the ground processing center. Nevertheless, it is often useful to have tools to evaluate image quality also at the final user level. Image quality is defined by some parameters, such as the radiometric resolution and its accuracy, represented by the noise level, and the geometric resolution and sharpness, described by the Modulation Transfer Function (MTF). This paper proposes a procedure to evaluate these image quality parameters; the procedure was implemented in a suitable software and tested on high resolution imagery acquired by the QuickBird, WorldView-1 and Cartosat-1 satellites.

Keywords: High Resolution Satellite Imagery quality; Modulation Transfer Function; actual resolution; noise; radiometric analysis.

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Figures

Figure 1.
Figure 1.
3 × 3 pixels window moving within the area by a 3 pixel step.
Figure 2.
Figure 2.
Cartosat-1 image DN histogram.
Figure 3.
Figure 3.
Simulated noisy image and a zoomed portion.
Figure 4.
Figure 4.
Simulated image noise level (σ) estimation at 5 and 10 percentile.
Figure 5.
Figure 5.
Explanation of the edge MTF estimation method.
Figure 6.
Figure 6.
Edge example.
Figure 7.
Figure 7.
Edge position.
Figure 8.
Figure 8.
Profile of a line.
Figure 9.
Figure 9.
Differentiation and edge position estimation.
Figure 10.
Figure 10.
Sub-pixel location estimation.
Figure 11.
Figure 11.
Fitted edge.
Figure 12.
Figure 12.
Line perpendicular to the edge.
Figure 13.
Figure 13.
Splines interpolating perpendicular lines.
Figure 14.
Figure 14.
Edge Spread Function.
Figure 15.
Figure 15.
Line Spread Function.
Figure 16.
Figure 16.
Empirical Edge Spread Function in blue color.
Figure 17.
Figure 17.
Line Spread Function from the empirical Edge Spread Function.
Figure 18.
Figure 18.
MTF at Nyquist frequency.
Figure 19.
Figure 19.
Full Width at Half Maximum.
Figure 20.
Figure 20.
Method to estimate FWHM.
Figure 21.
Figure 21.
(a) QB_CA_StdOr_Right image. (b) QB_CA_StdOr_Left image. In red the overlap area.
Figure 22.
Figure 22.
QuickBird Rome image.
Figure 23.
Figure 23.
WorldView-1 Rome image.
Figure 24.
Figure 24.
(a) CSAT1_CA_BandA.(b) CSAT1_CA_BandF. In red the overlap area.
Figure 25.
Figure 25.
(a) CSAT1_RM_BandA.(b) CSAT1_RM_BandF. In red the overlap area.
Figure 26.
Figure 26.
QB_CA signal-to-noise ratio (R) level estimation.
Figure 27.
Figure 27.
QB_RM signal-to-noise ratio (R) level estimation.
Figure 28.
Figure 28.
WV1_RM signal-to-noise ratio (R) level estimation.
Figure 29.
Figure 29.
CSAT1_RM signal-to-noise ratio (R) level estimation.
Figure 30.
Figure 30.
CSAT1_CA signal-to-noise ratio (R) level estimation.
Figure 31.
Figure 31.
(a) along-track direction edges example. (b) across-track direction edges example.
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References

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