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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Moving object detection in remote sensing image sequences has been widely used in military and civilian fields. However, the complex background of remote sensing images and the small sizes of moving objects bring great difficulties for effective detection. To solve this problem, we propose a real-time moving object detection method for remote sensing image sequences. This method works by fusing the semantic information from a single image extracted by the object detection branch with the motion information of multiple frames extracted by the motion detection branch. Specifically, in the motion detection branch, we design a motion feature enhancement module (MFE) to improve the interframe motion information. Then, we design a Motion Information Extraction network (MIE) to extract motion information. Finally, the moving object information is directly output by fusing the motion and semantic information extracted by the object detection branch. Based on the experimental results of the two datasets, the proposed method achieves an accuracy rate of 93.21%, a recall rate of 92.72%, an average frame rate of 25.25 frames (fps), and a performance of 96.71% in terms of [email protected]. The performance of the proposed method is better than that of other methods, and the overall detection effect is better; therefore, it meets the needs of the detection task.

Details

Title
A Dual-Input Moving Object Detection Method in Remote Sensing Image Sequences via Temporal Semantics
Author
Wang, Bo 1 ; Liu, Jinghong 1 ; Zhu, Shengjie 1   VIAFID ORCID Logo  ; Xu, Fang 2 ; Liu, Chenglong 2 

 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [email protected] (B.W.); [email protected] (S.Z.); [email protected] (F.X.); [email protected] (C.L.); University of Chinese Academy of Sciences, Beijing 100049, China 
 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [email protected] (B.W.); [email protected] (S.Z.); [email protected] (F.X.); [email protected] (C.L.) 
First page
2230
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2812741209
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.