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