Remote Sensing Videography: Potentials, Methods, and Applications
| dc.contributor.advisor | Qiu, Fang | |
| dc.contributor.advisor | Elliot, Euel | |
| dc.contributor.committeeMember | Briggs, Ronald | |
| dc.contributor.committeeMember | Chun, Yongwan | |
| dc.contributor.committeeMember | Cummings, Anthony | |
| dc.creator | Shi, Fan | |
| dc.date.accessioned | 2023-03-29T15:55:49Z | |
| dc.date.available | 2023-03-29T15:55:49Z | |
| dc.date.created | 2020-12 | |
| dc.date.issued | 2020-12-01T06:00:00.000Z | |
| dc.date.submitted | December 2020 | |
| dc.date.updated | 2023-03-29T15:55:50Z | |
| dc.description.abstract | In the last several years, new satellite sensors capable of capturing videos have been developed and launched. Unlike satellite images, the temporal resolution of a satellite video is determined by its frame rate. For example, the sensors onboard SkySat satellites can film panchromatic videos at the 1-meter spatial resolution with a frame rate of 30 frames per second (fps), which means the sensors’ temporal resolution is approximately 0.03 seconds. With the potential to detect and tracking moving objects, satellite videography provides a new perspective for Earth observation and enables important applications that may not be possible by using traditional remote sensing images. Moving object detection and tracking has been a hot topic in remote sensing. However, such research using satellite video data have been scarcely investigated. The first objective of this study was to utilize a satellite video to detect and tracking airplanes. To achieve this, two different methods have been developed. The first method includes an Improved Gaussian-based Background Subtractor (IPGBBS) algorithm for moving airplane detection and a Primary Scale Invariant Feature Transform keypoint matching (P-SIFT KM) algorithm for moving airplane tracking. The second method includes a Normalized Frame Difference Labeling (NFDL) algorithm for moving airplane detection and a template matching with improved similarity measures (TM-ISMs) for moving airplane tracking. The second objective of this study is to achieve traffic monitoring with satellite video data, which involves moving vehicle detection and tracking, vehicle motion property extraction, and traffic property extraction. The performance of the developed methods for moving airplane detection and tracking were compared with state-of-the-art approaches. Experimental results show that IPGBBS possesses higher detection accuracy than state-of-the-art approaches, and NFDL exhibits the highest detection accuracy. Moreover, TM-ISMs achieve notably higher tracking accuracy than TMTSMs. The developed method for traffic monitoring was tested on a satellite video of an urban area and demonstrated high accuracy for moving vehicle detection and tracking, which contributed to the effective extraction of both vehicle motion properties and traffic properties. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | ||
| dc.identifier.uri | https://hdl.handle.net/10735.1/9650 | |
| dc.language.iso | en | |
| dc.subject | Remote Sensing | |
| dc.title | Remote Sensing Videography: Potentials, Methods, and Applications | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.college | School of Economic, Political and Policy Science | |
| thesis.degree.department | Geospatial Information Sciences | |
| thesis.degree.grantor | The University of Texas at Dallas | |
| thesis.degree.name | PHD |
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