Remote sensing mass image automatic screening method based on optimized genetic algorithm

Abstract

The invention discloses a remote sensing mass image automatic screening method based on an optimized genetic algorithm, and the method comprises the steps: automatically screening an image solution meeting the demands through employing the genetic algorithm according to the requirements of a user for the coverage area, the resolution coverage rate, the cloud amount coverage rate and the timelinesscoverage rate; firstly, obtaining an initial image set, and then conducting coarse screening; then, modeling in a binary coding mode, and generating an image solution set through initialization; andthen calculating a comprehensive score, and calculating a probability according to the score for selection; solving a new image solution set for the selected image solution set through crossover variation; circulating the process, and selecting a final solution when a circulation ending condition is reached. According to the method, the problem of local optimization caused by a traditional greedyalgorithm is solved, the problem that the solving overlapping rate is too high or the coverage rate is low is avoided, weight configuration can be carried out on preferences in multiple aspects such as the coverage area, the cloud coverage rate and the timeliness coverage rate, and different requirements of different users for final screening results are met.

Description

technical field [0001] The invention belongs to the technical field of intelligent application of remote sensing images, and more specifically, relates to an automatic screening method for remote sensing massive images based on optimized genetic algorithms under different requirements for image area, timeliness, and quality in various business fields. Background technique [0002] With the military, civilian, and commercial earth observation satellites of the Gaofen series, Jilin series, Gaojing series, and resource series being gradually launched and put into use, the remote sensing image data gradually accumulated by various units has reached the level of one million, and With the introduction of the concepts of micro-nano satellites, constellations, and constellations, subsequent remote sensing image data will explode. [0003] According to business requirements, users generally need to obtain a subset of all image data in the area of ​​interest that meets the requirement...

AI Technical Summary

Problems solved by technology

However, the greedy algorithm is always a rule-based search method. This method randomly generates an initial position, traverses all search areas from the initial position, and selects the image with the best quality, time, and area ratio among all images at the current position to be included in the final solution. In , the final result is greatly constrained by the initial solution of the initial position, and it is prone to local optimum

Although this method solves the problem of search timeliness to a certain extent, it also introduces the problem of poor search effect.

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