MTF based remote sensor on-track automatic on-track parameter optimization method

Abstract

The invention provides an MTF based remote sensor on-track automatic on-track parameter optimization method. The MTF based remote sensor on-track automatic on-track parameter optimization method comprises the following steps of obtaining an original remote sensing image under the condition of current on-track parameter running; conducting frequency domain based denoising and MTF stretching operation on the original remote sensing image based on an MTF so as to improve image quality and judging whether the improved restored image is obviously improved or not; conducting next processing if the restored image is obviously improved; terminating on-track optimization operation if the restored image is not obviously improved, wherein image parameters of the quality-improved restored image extracted by means of an image batch processing method include edge energy, contrast ratio, definition, information entropy, detail energy, variance, image correlation, mean value, power spectrum and signal to noise ratio; inputting the image parameters extracted in the previous step into inversion models corresponding to focal length, phase shift and a forward parameter to obtain new parameters; using the new parameters to replace originally corresponding on-track parameters so as to produce new remote sensing image and performing optimization processing.

Description

technical field [0001] The invention relates to a method for instructing remote sensors to automatically optimize on-orbit parameters, in particular to a method for optimizing the quality of remote sensing images based on MTF and deriving satellite on-orbit parameters based on remote sensing image parameters, and belongs to the technical field of remote sensing image processing. Background technique [0002] Due to the influence of factors such as sensor performance degradation and atmospheric disturbance during the imaging process of remote sensing images, the image quality is degraded. Image restoration is a method of image preprocessing using knowledge about the nature of image degradation in order to suppress image degradation. The purpose of image restoration is to process correspondingly degraded (degraded) images to improve image quality. There may be many reasons for image degradation, such as the nonlinearity of the photoelectric sensor, the relative motion between...

AI Technical Summary

Problems solved by technology

[0003] However, the method of improving the quality of remote images based on the traditional image restoration method has many limitations, which makes it unable to achieve the desired effect. The main reasons are:

[0004] 1) The traditional image restoration method is a restoration method for a single image, which cannot compensate for the sensor performance as the sensor hardware ages, or the hardware and on-orbit parameter settings cannot be perfected due to changes in hardware performance. The adverse effects brought about by matching, that is, it cannot fundamentally solve the problem that causes the image quality of the remote sensor to decline;

[0005] 2) The scope of application of a single traditional image restoration method is limited, and the inducing factors leading to image quality degradation are various, so it cannot be guaranteed that it is applicable to all image degradation situations;

[0006] 3) At present, most image restoration methods are based on the assumption that the corresponding point spread function (PSF) is known, such as deconvolution method, Wiener filtering method, etc., so this assumption is difficult to guarantee its correctness and applicability

[0007] The limitations of traditional image restoration methods are obvious, and it cannot provide an effective mechanism to fundamentally solve the problem of remote sensing image quality degradation caused by remote sensor hardware degradation and on-orbit parameter mismatch.

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