Depth significance-based remote sensing image rapid retrieval method

A remote sensing image and salient technology, applied in the field of computer vision, can solve the problems of difficult to achieve accurate description and analysis of salient features of remote sensing images, indistinct background distinction, complex and changeable information, etc., to improve retrieval efficiency, save storage space, Easily expandable effects

Active Publication Date: 2017-06-30
BEIJING UNIV OF TECH
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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

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  • Depth significance-based remote sensing image rapid retrieval method
  • Depth significance-based remote sensing image rapid retrieval method
  • Depth significance-based remote sensing image rapid retrieval method

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Embodiment Construction

[0065] According to the above description, the following is a specific implementation process, but the protection scope of this patent is not limited to this implementation process.

[0066] Step 1: Construction of object detection model based on deep saliency

[0067]The salient area is subjectively understood as the area where the human eye focuses attention, and is closely related to the Human Visual System (HVS). Objectively speaking, it refers to a certain feature of the image, and there is a sub-area with the most obvious feature. Therefore, the crux of the saliency detection problem lies in feature learning and extraction. In view of the powerful function of deep learning in this aspect, the present invention uses the fully convolutional neural network for the salient detection problem, and proposes a multi-task salient target detection model based on the fully convolutional neural network. The model performs two tasks simultaneously: a saliency detection task and a se...

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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...

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Application Information

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IPC IPC(8): G06K9/34G06K9/46G06K9/62G06F17/30G06N3/04G06N3/08
CPCG06F16/583G06N3/084G06V10/267G06V10/462G06N3/045G06F18/22G06V20/05
Inventor 张菁梁西陈璐卓力耿文浩李嘉锋
Owner BEIJING UNIV OF TECH
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