A Fast Retrieval Method of Remote Sensing Image Based on Depth Saliency
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: 2020-08-28
BEIJING UNIV OF TECH
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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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[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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A deep saliency-based fast retrieval method for remote sensing images belongs to the field of computer vision, and specifically involves technologies such as deep learning, salient target detection, and image retrieval. The present invention takes remote sensing images as the research object and uses deep learning technology to study a fast retrieval method for remote sensing images. First, a fully convolutional neural network is used to construct a multi-task salient object detection model. The model simultaneously performs salient detection tasks and semantic segmentation tasks, and learns the deep salient features of remote sensing images during network pre-training. Then improve the deep network structure, add the hash layer to fine-tune the network, and learn the binary hash code of the remote sensing image. Finally, the similarity measurement is carried out by comprehensively using the salient features and hash codes. The invention is practical and has important application value for realizing accurate and efficient retrieval of 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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IPC IPC(8): G06K9/34G06K9/46G06K9/62G06F16/583G06N3/04G06N3/08
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