Remote sensing image text description generation method with multi-semantic-level attention capability

Abstract

The invention discloses a remote sensing image text description generation method with multi-semantic-level attention ability, and the method comprises the steps: carrying out the frame selection of potential object regions in a remote sensing image through the powerful object positioning capability of an object detection deep neural network, and combining a multi-layer visual element grid system according to the features of the regions; when description statements are generated, automatically concerning corresponding blocks in the multi-layer visual attention grid system according to context information, achieving more accurate visual attention focusing, bringing more appropriate vocabulary estimation, and describing remote sensing images more accurately and comprehensively. Compared with traditional single-scale sparse grid space attention block division, the method has more accurate focusing capability and joint multilayer semantic expression capability.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a method for generating text descriptions of remote sensing images. Background technique [0002] Remote sensing image text description generation is an emerging remote sensing image visual understanding task. This task requires the description generator to have an in-depth understanding of the semantic features of remote sensing images, and on this basis, it can output descriptive sentences that conform to the laws of human language. Compared with general image description generation, remote sensing image text description generation has the following difficulties: [0003] First, remote sensing images cover a large spatial scale, and the composition of ground elements is complex, making it difficult to fully understand; [0004] Second, the semantic distribution and density differences between images are obvious, such as deserts and cities; [0005] Third...

AI Technical Summary

Problems solved by technology

[0003] First, remote sensing images cover a large spatial scale, and the composition of ground elements is complex, making it difficult to fully understand;

[0004] Second, the semantic distribution and density differences between images are obvious, such as deserts and cities;

[0005] Third, it is difficult to model the relationship between ground objects and the whole world, and the complex organizational relationship between ground objects

However, due to the low grid resolution and fixed grid size, the remote sensing images with various element sizes cannot obtain sufficient reasonable and fine regional attention, and it is difficult to achieve ideal description sentence generation

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