A Method to Detect and Track Moving Airplanes from a Satellite Video
In recent years, satellites capable of capturing videos have been developed and launched to provide high definition satellite videos that enable applications far beyond the capabilities of remotely sensed imagery. Moving object detection and moving object tracking are among the most essential and ch...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/15/2390 |
| _version_ | 1797561375627673600 |
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| author | Fan Shi Fang Qiu Xiao Li Yunwei Tang Ruofei Zhong Cankun Yang |
| author_facet | Fan Shi Fang Qiu Xiao Li Yunwei Tang Ruofei Zhong Cankun Yang |
| author_sort | Fan Shi |
| collection | DOAJ |
| description | In recent years, satellites capable of capturing videos have been developed and launched to provide high definition satellite videos that enable applications far beyond the capabilities of remotely sensed imagery. Moving object detection and moving object tracking are among the most essential and challenging tasks, but existing studies have mainly focused on vehicles. To accurately detect and then track more complex moving objects, specifically airplanes, we need to address the challenges posed by the new data. First, slow-moving airplanes may cause foreground aperture problem during detection. Second, various disturbances, especially parallax motion, may cause false detection. Third, airplanes may perform complex motions, which requires a rotation-invariant and scale-invariant tracking algorithm. To tackle these difficulties, we first develop an Improved Gaussian-based Background Subtractor (IPGBBS) algorithm for moving airplane detection. This algorithm adopts a novel strategy for background and foreground adaptation, which can effectively deal with the foreground aperture problem. Then, the detected moving airplanes are tracked by a Primary Scale Invariant Feature Transform (P-SIFT) keypoint matching algorithm. The P-SIFT keypoint of an airplane exhibits high distinctiveness and repeatability. More importantly, it provides a highly rotation-invariant and scale-invariant feature vector that can be used in the matching process to determine the new locations of the airplane in the frame sequence. The method was tested on a satellite video with eight moving airplanes. Compared with state-of-the-art algorithms, our IPGBBS algorithm achieved the best detection accuracy with the highest F<sub>1</sub> score of 0.94 and also demonstrated its superiority on parallax motion suppression. The P-SIFT keypoint matching algorithm could successfully track seven out of the eight airplanes. Based on the tracking results, movement trajectories of the airplanes and their dynamic properties were also estimated. |
| first_indexed | 2024-03-10T18:13:04Z |
| format | Article |
| id | doaj.art-794b6f35eefd4b0bba3832fa67800950 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:13:04Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-794b6f35eefd4b0bba3832fa678009502023-11-20T07:55:13ZengMDPI AGRemote Sensing2072-42922020-07-011215239010.3390/rs12152390A Method to Detect and Track Moving Airplanes from a Satellite VideoFan Shi0Fang Qiu1Xiao Li2Yunwei Tang3Ruofei Zhong4Cankun Yang5Geospatial Information Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USAGeospatial Information Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USAGeospatial Information Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USAKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, ChinaBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, ChinaIn recent years, satellites capable of capturing videos have been developed and launched to provide high definition satellite videos that enable applications far beyond the capabilities of remotely sensed imagery. Moving object detection and moving object tracking are among the most essential and challenging tasks, but existing studies have mainly focused on vehicles. To accurately detect and then track more complex moving objects, specifically airplanes, we need to address the challenges posed by the new data. First, slow-moving airplanes may cause foreground aperture problem during detection. Second, various disturbances, especially parallax motion, may cause false detection. Third, airplanes may perform complex motions, which requires a rotation-invariant and scale-invariant tracking algorithm. To tackle these difficulties, we first develop an Improved Gaussian-based Background Subtractor (IPGBBS) algorithm for moving airplane detection. This algorithm adopts a novel strategy for background and foreground adaptation, which can effectively deal with the foreground aperture problem. Then, the detected moving airplanes are tracked by a Primary Scale Invariant Feature Transform (P-SIFT) keypoint matching algorithm. The P-SIFT keypoint of an airplane exhibits high distinctiveness and repeatability. More importantly, it provides a highly rotation-invariant and scale-invariant feature vector that can be used in the matching process to determine the new locations of the airplane in the frame sequence. The method was tested on a satellite video with eight moving airplanes. Compared with state-of-the-art algorithms, our IPGBBS algorithm achieved the best detection accuracy with the highest F<sub>1</sub> score of 0.94 and also demonstrated its superiority on parallax motion suppression. The P-SIFT keypoint matching algorithm could successfully track seven out of the eight airplanes. Based on the tracking results, movement trajectories of the airplanes and their dynamic properties were also estimated.https://www.mdpi.com/2072-4292/12/15/2390satellite videosmoving object detectionmoving object trackingSIFT |
| spellingShingle | Fan Shi Fang Qiu Xiao Li Yunwei Tang Ruofei Zhong Cankun Yang A Method to Detect and Track Moving Airplanes from a Satellite Video Remote Sensing satellite videos moving object detection moving object tracking SIFT |
| title | A Method to Detect and Track Moving Airplanes from a Satellite Video |
| title_full | A Method to Detect and Track Moving Airplanes from a Satellite Video |
| title_fullStr | A Method to Detect and Track Moving Airplanes from a Satellite Video |
| title_full_unstemmed | A Method to Detect and Track Moving Airplanes from a Satellite Video |
| title_short | A Method to Detect and Track Moving Airplanes from a Satellite Video |
| title_sort | method to detect and track moving airplanes from a satellite video |
| topic | satellite videos moving object detection moving object tracking SIFT |
| url | https://www.mdpi.com/2072-4292/12/15/2390 |
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