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| LiDAR 포인트 클라우드 분석× | 객체 기반 영상 분석 (OBIA)× | |
|---|---|---|
| 분야 | 원격탐사 | 원격탐사 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2002 | 2010 |
| 창시자≠ | Lefsky et al. | Thomas Blaschke |
| 유형≠ | Active remote sensing pipeline | Image segmentation and classification pipeline |
| 원전≠ | Lefsky, M. A., Cohen, W. B., Parker, G. G., & Harding, D. J. (2002). Lidar remote sensing for ecosystem studies. BioScience, 52(1), 19–30. DOI ↗ | Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16. DOI ↗ |
| 별칭 | Light Detection and Ranging, Airborne Laser Scanning, Terrestrial Laser Scanning, LiDAR Nokta Bulutu Analizi | Geographic Object-Based Image Analysis, GEOBIA, Object-Oriented Image Analysis, Nesne Tabanlı Görüntü Analizi |
| 관련≠ | 1 | 3 |
| 요약≠ | LiDAR (Light Detection and Ranging) Point-Cloud Analysis is an active remote sensing technique that measures distances by emitting laser pulses and recording the time for returns to reach the sensor. First systematically applied to ecosystem science by Lefsky, Cohen, Parker, and Harding in 2002, LiDAR produces dense three-dimensional point clouds that encode the precise vertical and horizontal structure of vegetation, terrain, and built environments at resolutions unachievable by passive optical sensors. | Object-Based Image Analysis (OBIA) is a remote sensing image processing paradigm that groups pixels into meaningful image objects before classification, rather than analysing each pixel independently. Formally articulated and consolidated by Thomas Blaschke in his landmark 2010 ISPRS review, OBIA draws on multiresolution segmentation algorithms and combines spectral, spatial, contextual, and textural object attributes to produce semantically rich land-cover maps from high-resolution imagery. |
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