Abstract
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.
Highlights
Natural disasters, such as earthquakes, tsunamis, floods and landslides, have shown a dramatically and globally increasing trend, both in frequency and intensity [1,2,3]
This verification is executed on two sides: the correspondence of the optimal threshold and the maximum F1-measure value, and the performance to determine the optimal threshold value based on adaptive threshold selection proposed in OFATS and Otsu, a classic thresholding way for binarization in image processing
The displacement was computed from two frames using optical flow estimation proposed in this paper
Summary
Natural disasters, such as earthquakes, tsunamis, floods and landslides, have shown a dramatically and globally increasing trend, both in frequency and intensity [1,2,3]. Change detection (CD) using remote sensing data can effectively capture changes before and after disasters [5,6,7], which has been widely used in various fields of natural disasters such as flood monitoring [8], landslide displacement tracking [9,10]. With the continuing growth of earth observation techniques and computer technology, massive amounts of remote sensing data for natural disaster with different spectral-spatial-temporal resolution are available for surveying and assessing changes in natural disaster, which greatly promotes the development of change detection methodologies. Many change detection approaches for natural disaster scenes have been proposed and they can be broadly divided into traditional and deep learning (DL)-based [15]. For traditional CD methods, the simplest approaches are algebra-based
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