Open AccessRemote Sensing Image Retrieval Based on Key Region Detection
Author(s) -
Famao ye,
Rengao Zhang,
Yuchi Xing,
Junwei Xin,
Dajun Li
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2216/1/012110
Subject(s) - computer science , artificial intelligence , feature (linguistics) , convolutional neural network , key (lock) , image retrieval , feature extraction , pattern recognition (psychology) , computer vision , object detection , image (mathematics) , reliability (semiconductor) , remote sensing , geography , philosophy , linguistics , power (physics) , physics , computer security , quantum mechanics
Remote sensing (RS) images usually describe large-scale natural geographical scenes with complex and rich background information, which will affect the retrieval performance of image features. How to reduce the background interference and improve the reliability of remote sensing image retrieval (RSIR) features is a problem that needs to be solved. In this paper, a RSIR method based on key region detection was proposed. Firstly, the ground objects of the image are extracted by a famous deep learning object detection model, a YOLO v5 model. Next, we extract the key region of the image according to these ground objects. Then, the image content in the key region is used to extract the retrieval feature by the convolutional neural networks (CNN) model, Resnet. Moreover, the weighted distance based on class probability is used to further improve retrieval performance. Our method utilizes the object detection capability of the YOLO model and the feature extraction capability of RESNET. Our method uses the target detection ability of the YOLO model and the feature extraction ability of RESNET to extract the retrieval feature of RS images. The experimental results on UCMD show that this method can improve the performance of RSIR.