Suppressing False Alarm in VideoSAR viaGradient-Weighted Edge Information
Abstract
:1. Introduction
2. Difference-Based Shadow Detection Algorithm
- Background Difference: Difference-based algorithm which extracts motion regions by thresholding the difference between the current frame and the background template. It should be noted that background template can be modeled via many methods like median average, mean average and Mixture Gauss [12]. Normally background difference algorithm could obtain decent effect when image sequence owns a stationary, smooth background and clear, large-size targets.
- Inter-Frame Difference: Difference-based algorithm which operates difference between adjacent frame and the current one. This algorithm holds the consensus that variation between adjacent frames is quite minor except for moving objects. In case of slow-moving objects, inter-frame difference can also be done between 2 longer-interval frames or among several frames (more than 2).
3. Analysis of Gradient Difference between False Alarm and Real Target in Edge Region
3.1. Shadow Region Analysis
3.2. Gradient Distinction between False Alarm and Real Target in Edge Region
- FA 1: False alarm introduced by the variation of the scattering characteristics. Due to the slow changing of the features, VideoSAR image sequence will inevitably jitter and cause false alarms.
- FA 2: False alarm introduced by the change of the viewing angle. The shadow of stationary target (eg: trees alongside the road) also changes with the viewing angle, bringing in some false alarms.
- FA 3: False alarm introduced by the target defocusing energy return, which regarding as bright spots (or lines) shifting in the video.
4. False Alarm Reduction Method via Gradient-Weighted Edge Information
5. Experiments and Analysis
6. Conclusions
Funding
Acknowledgments
Conflicts of Interest
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Frame Number | False Alarm Amount | Detection Rate | ||||
---|---|---|---|---|---|---|
Median | Unweighted | Gradient | Median | Unweighted | Gradient | |
10 | 1 | 1 | 1 | 0.57 | 0.57 | 0.57 |
70 | 2 | 2 | 1 | 0.86 | 0.86 | 0.71 |
130 | 1 | 1 | 0 | 1 | 1 | 1 |
190 | 1 | 1 | 0 | 0.5 | 1 | 1 |
250 | 0 | 0 | 0 | 0.5 | 0.5 | 0.5 |
310 | 2 | 2 | 1 | 1 | 1 | 1 |
370 | 1 | 1 | 1 | 0.75 | 0.75 | 0.75 |
430 | 4 | 3 | 2 | 0.75 | 0.75 | 0.75 |
500 | 1 | 0 | 0 | 0.83 | 0.83 | 0.83 |
570 | 4 | 5 | 2 | 0.67 | 0.67 | 0.67 |
630 | 3 | 1 | 0 | 1 | 1 | 0.8 |
690 | 3 | 1 | 0 | 1 | 1 | 1 |
750 | 5 | 4 | 2 | 0.5 | 0.5 | 0.5 |
810 | 6 | 6 | 3 | 0.8 | 0.8 | 0.8 |
870 | 3 | 2 | 1 | 0.67 | 0.67 | 0.67 |
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Li, Z.; Yu, A.; Dong, Z.; He, Z.; Yi, T. Suppressing False Alarm in VideoSAR viaGradient-Weighted Edge Information. Remote Sens. 2019, 11, 2677. https://doi.org/10.3390/rs11222677
Li Z, Yu A, Dong Z, He Z, Yi T. Suppressing False Alarm in VideoSAR viaGradient-Weighted Edge Information. Remote Sensing. 2019; 11(22):2677. https://doi.org/10.3390/rs11222677
Chicago/Turabian StyleLi, Zihan, Anxi Yu, Zhen Dong, Zhihua He, and Tianzhu Yi. 2019. "Suppressing False Alarm in VideoSAR viaGradient-Weighted Edge Information" Remote Sensing 11, no. 22: 2677. https://doi.org/10.3390/rs11222677