Push-scan type optical remote sensing load original image bad line automatic detection method and system

Abstract

The invention relates to a push-scan type optical remote sensing load original image bad line automatic detection method and system. The method comprises the following steps: reading an original image to be detected; setting an initial effective column of the original image, and by taking the initial valid column as a reference column, simultaneously scanning the two sides of the reference column; calculating a difference of a normalization exponent between a current column and the reference column in the scanning process, and comparing absolute value of the difference with a threshold T; if the absolute value of the difference is greater than the threshold T, marking that the current column is an invalid column; otherwise, marking that the current column is a valid column, taking the current column as a reference column of a next comparison process, and repeating the step until all columns are marked; and counting the quantity n of all invalid columns, and taking a ratio of the quantity n of all the invalid columns to the total quantity number N of the original image as a bad line rate. According to the method and system provided by the invention, bad lines in the original image in an optical remote sensing load in-orbit operation process can be automatically detected.

Description

technical field [0001] The invention relates to the intersecting field of on-orbit performance testing of optical remote sensing payloads and quality analysis and evaluation of remote sensing data, in particular to an automatic detection method and system for broken lines in original images of push-broom optical remote sensing payloads. Background technique [0002] The on-orbit performance test of optical remote sensing payload is a necessary means to ensure the payload performance and data quality after the payload is launched. By detecting the bad lines of the original image, the bad pixels in the detection components can be found in time, and it is also a good example for the remote sensing data preprocessing process. Bad line repair provides effective reference to ensure the quality of data products. [0003] At present, in the laboratory environment, the background and blackbody are mainly used to detect the dead pixels and bad pixels in the detector. After the operati...

AI Technical Summary

Problems solved by technology

This method can detect the fringes existing in the image data, and is suitable for fringe detection on the image after relative radiation correction. However, since the original image has not been corrected for relative radiation, the response difference between each detection pixel has not been eliminated, so this method The method will mistakenly detect effective pixels as stripes, and cannot achieve the purpose of detecting bad lines (that is, bad pixels)

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