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Quantitative measurement for the homogeneity and contrast of the step edges in satellite image point spread function estimation This publication appears in: International Journal of Remote Sensing Authors: F. Chen, J. Ma, J. C-W Chan and D. Yan Volume: 32 Issue: 22 Pages: 7179-7201 Publication Date: Oct. 2011
Abstract: It is important to measure the spatial performance of an in-flight satellite sensor to determine if any spatial resolution degradations occur over time then some compensation methods can be adopted to improve the image quality. In satellite on-orbit measurements of spatial resolution, the performance of the sensors can be objectively evaluated through the point spread function (PSF) or modulation transfer function (MTF). The well-known image-based PSF derivation method of sampling along a slanted linear step structure from the degraded image is widely used in the satellite image restoration area. Due to the non-uniformity of the linear ground objects, generally, the operators have to pick out good profiles from all the profiles along the edge in order to increase the estimation accuracy. However, only some qualitative criteria have been proposed in the literature to instruct researchers how to pick out good profiles. In this article, we propose a method to quantitatively measure the homogeneity and contrast of the candidate linear structures in order to extract the optimal profiles. This method first automatically detects and locates the slanted linear step edge object in the image. Then it measures the homogeneity and the contrast at each location along the edge by using two adjacent square regions on both sides of the linear step structure. The locations that have high measurement values are regarded as the optimal locations. Finally, the optimal profiles can be extracted at these edge locations to derive an accurate PSF estimation by differentiation. This method makes an automatic optimal step edge detection method for PSF derivation to be practical. Experiment results on simulated and real images are provided. This article shows that using only the 'optimal' profiles can play a real and important role and can achieve a good PSF estimation result.
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