A Two-Stage Optimization Strategy for Fuzzy Object-Based Analysis Using Airborne LiDAR and High-Resolution Orthophotos for Urban Road Extraction
Table 2
Fuzzy rule sets developed by analyzing the decision tree and the fuzzy -mean clustering algorithms.
Land cover class
Fuzzy rules
Building
IF nDSM is High AND is High THEN Object is Building IF nDSM is High AND Density is High AND is Low AND Rectangular Fit is High THEN Object is Building IF nDSM is High AND GLCM-Hom is High AND Rectangular Fit is High THEN Object is Building IF Intensity is High AND nDSM is High AND Rectangular Fit is High THEN Object is Building
Road
IF nDSM is Low AND is High AND Density is Low THEN Object is Road IF nDSM is Low AND is Low AND GLCM-Hom is Low THEN Object is Road IF Intensity is High AND nDSM is Low AND is Low AND Elongation is High THEN Object is Road IF nDSM is Low AND is High AND Density is Low THEN Object is Road
Urban tree
IF nDSM is High AND is Low THEN Object is Urban Tree IF nDSM is High AND Density is High AND is Low AND Rectangular Fit is Low THEN Object is Urban Tree IF nDSM is High AND GLCM-Hom is High AND Rectangular Fit is Low THEN Object is Urban Tree IF Intensity is High AND nDSM is High AND Rectangular Fit is Low THEN Object is Urban Tree
Grass land
IF Intensity is High AND nDSM is Low AND is High THEN Object is Grass Land IF Intensity is High AND nDSM is Low AND is Low THEN Object is Grass Land
Bare land
IF nDSM is Low AND is High AND Density is High THEN Object is Bare Land IF nDSM is Low AND GLCM-Hom is High AND Shape Index is Low THEN Object is Bare Land IF nDSM is Low AND is High AND Density is High THEN Object is Bare Land
Water
IF nDSM is Low AND is Low AND GLCM-Hom is High AND is High THEN Object is Water IF Intensity is High AND nDSM is Low AND is Low AND is Low THEN Object is Water
Shadow
IF Intensity is Low AND nDSM is Low AND is High THEN Object is Shadow IF nDSM is Low AND GLCM-Hom is High THEN Object is Shadow