Depth significance-based remote sensing image rapid retrieval method

Abstract

A depth significance-based remote sensing image rapid retrieval method is disclosed and belongs to the field of computer vision. The method disclosed in the invention specifically relates to technologies such as in-depth learning, significance object detection, image retrieval and the like. According the method, remote sensing images are research objects, and in-depth learning technologies are used for researching a remote sensing image rapid retrieval method. A full convolution neural network is adopted for constructing a multitask significance object detection model which is used for doing significance detection tasks and semantic segmentation tasks at the same time, and depth significance characteristics of the remote sensing images are learnt in network pre-training processes. A depth network structure is improved, a Hash layer fine tuning network is added, and binary system Hash codes of the remote sensing images can be obtained via learning. Significance characteristics and the Hash codes are used comprehensively for similarity measurement. The method disclosed in the invention is of high application value for realizing accurate, highly efficient and feasible retrieval of the remote sensing images.

Description

technical field [0001] The present invention takes remote sensing images as the research object, and uses the latest research results in the field of artificial intelligence - deep learning technology to study a fast retrieval method for remote sensing images. Firstly, the fully convolutional neural network is used to build a multi-task salient target detection model, and the depth salient features of remote sensing images are calculated; then the deep network structure is improved, and the hash layer is added to learn the binary hash code; finally, the salient features and the hash are comprehensively used Xima realizes accurate and fast retrieval of remote sensing images. The invention belongs to the field of computer vision, and specifically relates to technologies such as deep learning, salient target detection and image retrieval. Background technique [0002] Remote sensing image data, as the basic data in the three major spatial information technologies of geographic...

AI Technical Summary

Problems solved by technology

Compared with ordinary image retrieval, the information contained in remote sensing images is complex and changeable, and the target is small and not clearly distinguished from the background. If the traditional saliency detection method is still used, it will be difficult to accurately describe and analyze the salient features of remote sensing images.

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