Shoreline Based Feature Extraction and Optimal Feature Selection for Segmenting Airborne LiDAR Intensity Images

Modern airborne laser swath mapping (ALSM) systems measure both elevation and reflection intensity of the terrain. However, this intensity has been under utilized as a feature for image classification because it does not represent true terrain radiance. In areas with minimal topographic relief, such as beaches, we show that segmenting intensity images rather than elevation images has great potential for scene analysis. Several intensity-based features are extracted from ALSM data collected along a beach and partitioned into three classes to detect the water line. Class-conditional probability density functions are estimated for each feature to asses which are most informative. Results indicate significant class separation using centroidal features. Their classification performance is evaluated using a naive Bayes classifier and the area under receiver operating characteristic curves. The method presented provides a novel feature extraction and a systematic feature selection procedure for high-resolution ALSM intensity data.