Optimal Allocation Algorithm of Geological and Ecological High-resolution Remote Sensing Monitoring Sampling Points
The purpose of this study is to solve the problem of unsatisfactory image representation of monitoring sampling points in high-resolution remote sensing due to the complexity of geological ecology. Firstly, three algorithms used in remote sensing technology were introduced, that is, extraction algor...
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| Veröffentlicht in: | Earth sciences research journal 2020-03, Vol.24 (1), p.105-110 |
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| description | The purpose of this study is to solve the problem of unsatisfactory image representation of monitoring sampling points in high-resolution remote sensing due to the complexity of geological ecology. Firstly, three algorithms used in remote sensing technology were introduced, that is, extraction algorithm of monitoring sampling point (selective search algorithm), discriminant algorithm (support vector machine) and BING algorithm. Then, the BING algorithm was improved. Finally, the superiority of the improved BING algorithm was verified through experimental data set. The results showed that selective search algorithm could generate more candidate windows in remote sensing image and had better adaptability. The improved algorithm had higher quality of candidate windows extracted from remote sensing images. Although the IBING algorithm could greatly improve the extraction speed of remote sensing, the detection time of each image became larger. Such testing times were still acceptable. Therefore, in this research, the allocation algorithm of geological and ecological high-resolution remote sensing monitoring sampling points was optimized, which had a good guiding significance for the application of remote sensing technology in geological and ecological research. |
| doi_str_mv | 10.15446/esrj.v24n1.85531 |
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Firstly, three algorithms used in remote sensing technology were introduced, that is, extraction algorithm of monitoring sampling point (selective search algorithm), discriminant algorithm (support vector machine) and BING algorithm. Then, the BING algorithm was improved. Finally, the superiority of the improved BING algorithm was verified through experimental data set. The results showed that selective search algorithm could generate more candidate windows in remote sensing image and had better adaptability. The improved algorithm had higher quality of candidate windows extracted from remote sensing images. Although the IBING algorithm could greatly improve the extraction speed of remote sensing, the detection time of each image became larger. Such testing times were still acceptable. Therefore, in this research, the allocation algorithm of geological and ecological high-resolution remote sensing monitoring sampling points was optimized, which had a good guiding significance for the application of remote sensing technology in geological and ecological research.</description><identifier>ISSN: 1794-6190</identifier><identifier>EISSN: 2339-3459</identifier><identifier>DOI: 10.15446/esrj.v24n1.85531</identifier><language>eng</language><publisher>Bogata: Universidad Nacional de Colombia, Departamento de Geociencias</publisher><subject>Adaptability ; Algorithms ; Detection ; Ecological research ; Ecology ; Geological research ; Geology ; GEOSCIENCES, MULTIDISCIPLINARY ; High resolution ; Image detection ; Mayors ; Remote monitoring ; Remote sensing ; Resolution ; Sampling ; Search algorithms ; Search engines ; Support vector machines ; Technology ; Time ; Windows</subject><ispartof>Earth sciences research journal, 2020-03, Vol.24 (1), p.105-110</ispartof><rights>COPYRIGHT 2020 Universidad Nacional de Colombia, Departamento de Geociencias</rights><rights>2020. 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Such testing times were still acceptable. 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| subjects | Adaptability Algorithms Detection Ecological research Ecology Geological research Geology GEOSCIENCES, MULTIDISCIPLINARY High resolution Image detection Mayors Remote monitoring Remote sensing Resolution Sampling Search algorithms Search engines Support vector machines Technology Time Windows |
| title | Optimal Allocation Algorithm of Geological and Ecological High-resolution Remote Sensing Monitoring Sampling Points |
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