High-performance implementation method for multi-scale segmentation of remote sensing images

Abstract

The invention provides a high-performance implementation method for multi-scale segmentation of remote sensing images, and in particular a method for implementing fast and multi-scale image segmentation of large amount of remote sensing images and establishing the structural relationship of segmentation results in the process of high-resolution remote sensing image information extraction. The method comprises the following steps: on the basis of analyzing the implementation procedure of the algorithm and finding out the computing-intensive segment of the algorithm, realizing the parallel segmentation of the algorithm-intensive segment based on MPI and OMP models, and carrying out date join on the parallel segmentation results; implementing the multi-scale image segmentation by storing the initial segmentation results of the algorithm and merging the subsequent multi-scale remote sensing images; and establishing a multi-scale object topological relation model. The generated multi-scale segmentation region and corresponding hierarchical relationship can be applied to various services; and the corresponding implementation method is applicable to various segmentation algorithms such as mean shift and the like, and can greatly improve the data amount processed by algorithm and the processing efficiency.

Description

technical field [0001] The present invention relates to remote sensing image processing technology and remote sensing image information extraction method, in particular, to multi-scale segmentation of remote sensing images and its high-performance implementation method, and the establishment of scale topological relationship of segmentation results. The present invention is applicable to various remote sensing images High performance implementation of the split method. Background technique [0002] The object-oriented remote sensing image analysis method has been widely used in current remote sensing image processing, information extraction and analysis, target recognition, classification and other applications, and has become an important method for remote sensing image processing and analysis, especially high-resolution remote sensing image processing and analysis. Among them, object extraction, feature calculation and feature analysis are important steps in the process of...

AI Technical Summary

Problems solved by technology

[0004] In the multi-scale image segmentation process, due to the large amount of remote sensing image data and complex algorithms, the corresponding segmentation algorithm needs to consume a large amount of memory. On the one hand, the remote sensing image segmentation process requires a large amount of computing resources, resulting in low efficiency. , on the other hand, many images cannot be segmented because they require a large amount of memory, so many practical applications need to build a more suitable high-performance image segmentation environment to realize the segmentation of remote sensing images; for the multi-scale segmentation of remote sensing images. Due to the need for multi-scale segmentation results, it is necessary to quickly realize multi-scale segmentation of images (that is, multi-scale merging of regions), and establish hierarchical correspondence between different scales, so as to facilitate scale conversion between layers and reuse of corresponding knowle

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