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Анализ облаков точек LiDAR×Объектно-ориентированный анализ изображений (OBIA)×
ОбластьДистанционное зондированиеДистанционное зондирование
СемействоProcess / pipelineProcess / pipeline
Год появления20022010
Автор методаLefsky et al.Thomas Blaschke
ТипActive remote sensing pipelineImage segmentation and classification pipeline
Основополагающий источникLefsky, 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 ↗
Другие названияLight Detection and Ranging, Airborne Laser Scanning, Terrestrial Laser Scanning, LiDAR Nokta Bulutu AnaliziGeographic Object-Based Image Analysis, GEOBIA, Object-Oriented Image Analysis, Nesne Tabanlı Görüntü Analizi
Связанные13
СводкаLiDAR (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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  2. 1 Источники
  3. PUBLISHED
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ScholarGateСравнение методов: LiDAR Analysis · Object-Based Image Analysis. Получено 2026-06-15 из https://scholargate.app/ru/compare