Abstract
Abstract. Real-time change detection and analysis of natural disasters is of great importance to emergency response and disaster rescue. Recently, a number of video satellites that can record the whole process of natural disasters have been launched. These satellites capture high resolution video image sequences and provide researchers with a large number of image frames, which allows for the implementation of a rapid disaster procedure change detection approach based on deep learning. In this paper, pixel change in image sequences is estimated by optical flow based on FlowNet 2.0 for quick change detection in natural disasters. Experiments are carried out by using image frames from Digital Globe WorldView in Indonesia Earthquake took place on Sept. 28, 2018. In order to test the efficiency of FlowNet 2.0 on natural disaster dataset, 7 state-of-the-art optical flow estimation methods are compared. The experimental results show that FlowNet 2.0 is not only robust to large displacements but small displacements in natural disaster dataset. Two evaluation indicators: Root Mean Square Error (RMSE) and Mean Value are used to record the accuracy. For estimation error of RMSE, FlowNet 2.0 achieves 0.30 and 0.11 pixels in horizontal and vertical direction, respectively. The error in horizontal error is similar to other algorithms but the value in vertical direction is significantly lower than them. And the Mean Value are 1.50 and 0.09 pixels in horizontal and vertical direction, which are most close to the ground truth comparing to other algorithms. Combining the superiority of computing time, the paper proves that only the approach based on FlowNet 2.0 is able to achieve real-time change detection with higher accuracy in the case of natural disasters.
Highlights
Rapid detection and visualization of change in natural disaster regions are vital for swift response to rescue and relief
The video gives a glimpse at the damage in the worst-hit area where soil liquefaction causes the ground to boil. 58 frames of the video have been extracted as the input image sequence for change detection and visualization
The selected image frames are tested based on classic optical flow estimation methods and FlowNet 2.0
Summary
Rapid detection and visualization of change in natural disaster regions are vital for swift response to rescue and relief. As one of the key technologies in disaster evaluation, change detection refers to identifying the set of pixels that are significant and possibly subtle changes between the image sequences (Fernàndez-Prieto et al, 2011). The basic principle of change detection methods takes multi-temporal images as input and outputs a binary image B, where a set of different pixels x between the pre- and post-image of the sequence would be valued according to the following generic rule: If there is a distinct change at pixel x in the last sequence, B(x) could be assigned a value of 1, otherwise, it is 0. Changes happen continuously and gradually, motion change can lead to appearance change owing to the different time intervals
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