지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
This paper indirectly created high density point cloud data using unmanned aerial vehicle image. Then, we tried to suggest new concept of classification technique where particular objects from point cloud data can be selectively classified. For this, we established the classification technique that can be used as search factor in classifying color information in point cloud data. Then, using suggested classification technique, we implemented object classification and analyzed classification accuracy by relative comparison with self-created proof resource. As a result, the possibility of point cloud data classification was observable using the image’s information. Furthermore, it was possible to classify particular object’s point cloud data in high classification accuracy.
목차
Abstract
1. Introduction
2. Point Cloud Data Classification Technique Based on Image Color Information
3. Point Cloud Data Classification and Accuracy Analysis
4. Conclusion
References
참고문헌 (11)
참고문헌 신청
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Edward, W. and Jason, G. (2007), Integrating LiDAR data and multispectral imagery for enhanced classification of rangeland vegetation: a meta analysis, Remote Sensing of Environment, Vol. 111, No. 1, pp. 11-24.
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Hutton, C. and Brazier, R. (2012), Quantifying riparian zone structure from airborne LiDAR: vegetation: filtering, anisotropic interpolation, and uncertainty propagation, Journal of Hydrology, Vol. 442/443, pp. 36-45.
Jeon, W. and Choi, B. (2013), A study on the automatic detection of railroad power lines using LiDAR fata and RANSAC algorithm, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 31, No. 4, pp. 331-339. (in Korean with English abstract)
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UCI(KEPA) : I410-ECN-0101-2018-533-001259064