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Open AccessArticle

A Novel Change Detection Method for Natural Disaster Detection and Segmentation from Video Sequence

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Technology and Engineering Center for Space Utilization, Chinese Academy of Science, Beijing 100094, China
3
Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(18), 5076; https://doi.org/10.3390/s20185076
Received: 22 July 2020 / Revised: 27 August 2020 / Accepted: 2 September 2020 / Published: 7 September 2020
Change detection (CD) is critical for natural disaster detection, monitoring and evaluation. Video satellites, new types of satellites being launched recently, are able to record the motion change during natural disasters. This raises a new problem for traditional CD methods, as they can only detect areas with highly changed radiometric and geometric information. Optical flow-based methods are able to detect the pixel-based motion tracking at fast speed; however, they are difficult to determine an optimal threshold for separating the changed from the unchanged part for CD problems. To overcome the above problems, this paper proposed a novel automatic change detection framework: OFATS (optical flow-based adaptive thresholding segmentation). Combining the characteristics of optical flow data, a new objective function based on the ratio of maximum between-class variance and minimum within-class variance has been constructed and two key steps are motion detection based on optical flow estimation using deep learning (DL) method and changed area segmentation based on an adaptive threshold selection. Experiments are carried out using two groups of video sequences, which demonstrated that the proposed method is able to achieve high accuracy with F1 value of 0.98 and 0.94, respectively. View Full-Text
Keywords: change detection; natural disasters; deep learning; threshold selection; optical flow estimation change detection; natural disasters; deep learning; threshold selection; optical flow estimation
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MDPI and ACS Style

Qiao, H.; Wan, X.; Wan, Y.; Li, S.; Zhang, W. A Novel Change Detection Method for Natural Disaster Detection and Segmentation from Video Sequence. Sensors 2020, 20, 5076.

AMA Style

Qiao H, Wan X, Wan Y, Li S, Zhang W. A Novel Change Detection Method for Natural Disaster Detection and Segmentation from Video Sequence. Sensors. 2020; 20(18):5076.

Chicago/Turabian Style

Qiao, Huijiao; Wan, Xue; Wan, Youchuan; Li, Shengyang; Zhang, Wanfeng. 2020. "A Novel Change Detection Method for Natural Disaster Detection and Segmentation from Video Sequence" Sensors 20, no. 18: 5076.

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