Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms

This paper presents a new Bayesian clustering method to analyse remotescenes sensed via multispectral Lidar measurements. To a firstapproximation, each Lidar waveform mainly consists of the temporalsignature of the observed target, which depends on the wavelengthof the laser source considered and which is corrupted by Poissonnoise. By sensing the scene at several wavelengths, we expect amore accurate target range estimation and a more efficient spectralanalysis of the scene. Thanks to its spectral classification capability,the proposed hierarchical Bayesian model, coupled with an efficientMarkov chain Monte Carlo algorithm, allows the estimation of depthimages together with reflectivity-based scene segmentation images.The proposed methodology is illustrated via experiments conductedwith real multispectral Lidar data.