Object detection using optical and LiDAR data fusion

Fusion of aerial optical and LiDAR data has been a popular problem in remote sensing as they carry complementary information for object detection. We describe a stratified method that involves separately thresholding the normalized digital surface model derived from LiDAR data and the normalized difference vegetation index derived from spectral bands to obtain candidate image parts that contain different object classes, and incorporates spectral and height data with spatial information in a graph cut framework to segment the rest of the image where such separation is not possible. Experiments using a benchmark data set show that the performance of the proposed method that uses small amount of supervision is compatible with the ones in the literature. © 2016 IEEE.