Efficient target identification processing method of high-resolution remote sensing image in context

Abstract

The invention discloses an efficient target identification processing method of a high-resolution remote sensing image in a context. The efficient target identification processing method comprises the special steps as follows: 1. graying original image data, and setting an empirical value of a target object, so as to obtain a gray threshold value and a minimum bounding rectangle of the target object; 2. carrying out encoding on the minimum bounding rectangle by using a decimal Morton code, so as to establish a linear quad-tree; 3. extracting and generating a border polygon of the target object; 4. cutting an original image target data block by using the border polygon of the target object; 5. dynamically establishing a dynamic virtual clustering machine, and distributing the image target data block to a corresponding calculation node and calculating according to demands; and 6. collecting results of the calculation nodes in each level by a cluster main control calculation node, and returning to be at a corresponding position of the original image data according to an address code in the step 2 according to the step in order. With the adoption of the efficient processing method provided by the invention, the image data calculation is automatically blocked and distributed; the man-machine interaction is reduced; and meanwhile, the image treatment efficiency is further improved by using a thought of local calculation.

Description

technical field [0001] The invention belongs to the field of remote sensing image application technology, and mainly relates to an efficient processing method for high-resolution remote sensing image target recognition under a large background. Background technique [0002] The continuous development of remote sensing technology is accompanied by the improvement of space, time and space, and resolution of remote sensing images, resulting in a large increase in the amount of data for each image, and a corresponding increase in computing load. According to the regularity and correlation characteristics of image data storage, its algorithm also has the characteristics of consistency, neighborhood, and row order, which creates good conditions for image parallel computing. In addition, with the development of computer technology, research on the storage and application of remote sensing image data at home and abroad has turned to technologies such as distributed cluster computing...

AI Technical Summary

Problems solved by technology

Among them, there are few researches on the data parallelism of the cluster system and the idea of ​​using local processing for the division of image data and the data distribution of cluster computing nodes. Although the existing methods can improve the performance of image processing to a certain extent, if appropriate With a reasonable mechanism at the right time, there will still be room for improvement in terms of efficient processing performance

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