Semantic Generation Method of Remote Sensing Image Based on Fast Region Convolutional Neural Network

Abstract

The present invention proposes a remote sensing image semantic generation method based on a fast regional convolutional neural network, which mainly solves the problem that the existing technology cannot obtain the relationship between objects in the image and the relationship between the object and the image as a whole. The implementation plan is: construct training sample set and test sample set; use fast regional convolutional neural network to extract image features of high-resolution remote sensing images; use bidirectional cyclic neural network to extract text features of corresponding sentences; use probability-based graph-text The matching model matches image features and text features; uses the matched graphic and text features to train the long short-term memory network, and then realizes the semantic generation of high-resolution remote sensing images. The invention fully considers the characteristics of complex background and various objects of the remote sensing image, improves the semantic generation result of the remote sensing image, and can be used for image retrieval or scene classification.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image semantic generation method, which can be used to automatically describe the contents of remote sensing images. Background technique [0002] The understanding and description of remote sensing image content can provide decision-level support for remote sensing applications, and has a wide range of practical application values. For example, in the field of military reconnaissance, existing research algorithms can quickly identify important military targets such as ports, airports, and ships from remote sensing images. The understanding and description of remote sensing image content can accurately and comprehensively understand large and wide battlefield images, so as to realize real-time interpretation of battlefield geographical environment and dynamic intelligence generation. In terms of civilian use, the understanding and description of remote sensing image ...

AI Technical Summary

Problems solved by technology

This method can obtain superficial semantic information for auxiliary recognition, but the method is not systematic enough, and stays at the stage of target positioning and recognition, and cannot obtain the relationship between the target in the image, nor the relationship between the target and the image as a whole. , affecting the accuracy of subsequent tasks such as image detection and scene classification

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