A hybrid conditional random field for estimating the underlying ground surface from airborne LiDAR data

IEEE Transactions on Geoscience and Remote Sensing

Volumn 47, Issue 8, 2009, Pages 2913-2922

  Lu, Wei Lwun ^a   Murphy, Kevin P ^a   Little, James J ^a   Sheffer, Alla ^a   Fu, Hongbo ^b  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Conditional random fields (CRFs); Digital terrain model (DTM); Light detection and ranging (LiDAR) data filtering

Indexed keywords

AIRBORNE LIDAR DATA; CONDITIONAL RANDOM FIELD; CONDITIONAL RANDOM FIELDS (CRFS); DIGITAL SURFACE MODELS; DIGITAL TERRAIN MODEL; DIGITAL TERRAIN MODEL (DTM); EARTH SURFACE; EXPECTATION-MAXIMIZATION ALGORITHMS; FLOOD MODELING; GAUSSIAN RANDOM FIELDS; GROUND SURFACES; LANDSLIDE PREDICTION; LARGE REGIONS; LATENT VARIABLE; LEARNING-BASED APPROACH; LIDAR MEASUREMENTS; LIGHT DETECTION AND RANGING; LIGHT DETECTION AND RANGING (LIDAR) DATA FILTERING; MACHINE-LEARNING; MAXIMUM A POSTERIORI; POINT CLOUD; PROBABILISTIC MODELS; SURFACE HEIGHT; TERRAIN FEATURES;

CONTENT BASED RETRIEVAL; IMAGE SEGMENTATION; LEARNING ALGORITHMS; OPTICAL RADAR; RANGE FINDING; SUPERVISED LEARNING; SURFACE TOPOGRAPHY; TEMPERATURE CONTROL;

THREE DIMENSIONAL;

AIRBORNE SENSOR; ALGORITHM; DATA PROCESSING; DATA SET; DIGITAL TERRAIN MODEL; GAUSSIAN METHOD; GROUND CONDITIONS; LIDAR; TERRAIN;

EID: 67949104959     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2009.2017738     Document Type: Article

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