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Urban area object-based classification by fusion of hyperspectral and LiDAR data

This research presents a novel strategy for hyperspectral and LiDAR data fusion to generate accurate LC/LU maps with object based classification method. Unlike conventional object-based strategies (hierarchical and multilevel models), in the proposed method, classification has been performed in an iterative Segmentation-Classification-Merging (SCM) process. In each iteration, image objects are extracted by using their spectral, height, geometric and class-related characteristics based on data availability, class importance and higher extraction capability. Results indicate great overall accuracy of 97.33% and a kappa coefficient of 0.9710.