Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data

Remote Sensing of Environment

Volumn 117, Issue , 2012, Pages 102-113

  Im, Jungho ^a   Lu, Zhenyu ^a   Rhee, Jinyoung ^a   Quackenbush, Lindi J ^a  

^a STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

Artificial immune networks; Decision regression trees; Impervious surfaces; Lidar; WorldView 2

Indexed keywords

ARTIFICIAL IMMUNE NETWORKS; DECISION/REGRESSION TREES; ENVIRONMENTAL RESOURCES; HIERARCHICAL CLASSIFICATION; HIGH RESOLUTION; HIGH SPATIAL RESOLUTION; IMPERVIOUS SURFACE; INTEGRATED METHOD; LANDSAT TM DATA; LIDAR DATA; MACHINE-LEARNING; MANAGEMENT APPLICATIONS; MODEL SENSITIVITY; MULTI-SENSOR DATA; REGRESSION TREES; SMALL SAMPLE SIZE; SPATIAL RESOLUTION; SPATIAL SCALE; SUB PIXELS; TRAINING SAMPLE; WORLDVIEW-2;

BINARY TREES; DECISION TREES; FORESTRY; GROUNDWATER; GROUNDWATER RESOURCES; IMAGE RESOLUTION; INFORMATION MANAGEMENT; OPTICAL RADAR; REGRESSION ANALYSIS; SAMPLING; SENSOR DATA FUSION; SENSORS;

TREES (MATHEMATICS);

DECISION MAKING; GROUNDWATER RESOURCE; LANDSAT THEMATIC MAPPER; LIDAR; OPTIMIZATION; PIXEL; REGRESSION ANALYSIS; SATELLITE DATA; SPATIAL ANALYSIS; SPATIAL RESOLUTION; WATER MANAGEMENT;

ARTIFICIAL INTELLIGENCE; DECISION MAKING; FORESTRY; GROUND WATER; NEURAL NETWORKS; REGRESSION ANALYSIS; SATELLITES; SENSORS; SURFACE TREATMENT; SYNTHESIS;

EID: 84855469971     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2011.06.024     Document Type: Article

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