Remote sensing image semantic generation method based on fast region convolutional neural network

Abstract

The present invention provides a remote sensing image semantic generation method based on a fast region convolutional neural network. The problems are overcome that relation between images in an imagecannot be obtained and a relation between a target and the whole image cannot be obtained. The method comprises the steps of: constructing a training sample set and a test sample set; employing a fast region convolutional neural network to extract image features of a high-resolution remote sensing image; employing a bidirectional recurrent neural network to extract text features of correspondingsentences; employing an image-text matching model based on probability to match the image features with the text features; and employing the matching image-text features to perform training for a long-and-short time memory network for training so as to achieve semantic generation of the high-resolution remote sensing image. The remote sensing image semantic generation method fully considers the features of complex remote sensing image backgrounds and multiple targets so as to improve the result of remote sensing image semantic generation, 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

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