Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms using Gamma Markov Random Fields

Journal Article · Wed May 10 00:00:00 EDT 2017 · IEEE Transactions on Computational Imaging (CD-ROM)

DOI:https://doi.org/10.1109/TCI.2017.2703144· OSTI ID:1367663

Altmann, Yoann  ^[1]; Maccarone, Aurora ^[1]; McCarthy, Aongus ^[1]; Newstadt, Gregory ^[2]; Buller, Gerald S. ^[1]; McLaughlin, Stephen  ^[1]; Hero, Alfred ^[3]

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1. Heriot-Watt Univ., Edinburgh, Scotland (United Kingdom). School of Engineering and Physical Sciences
2. Google Inc., Pittsburgh, PA (United States)
3. Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Electrical Engineering and Computer Science

Here, this paper presents a new Bayesian spectral un-mixing algorithm to analyse remote scenes sensed via sparse multispectral Lidar measurements. To a first approximation, in the presence of a target, each Lidar waveform consists of a main peak, whose position depends on the target distance and whose amplitude depends on the wavelength of the laser source considered (i.e, on the target reflectivity). Besides, these temporal responses are usually assumed to be corrupted by Poisson noise in the low photon count regime. When considering multiple wavelengths, it becomes possible to use spectral information in order to identify and quantify the main materials in the scene, in addition to estimation of the Lidar-based range profiles. Due to its anomaly detection capability, the proposed hierarchical Bayesian model, coupled with an efficient Markov chain Monte Carlo algorithm, allows robust estimation of depth images together with abundance and outlier maps associated with the observed 3D scene. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data acquired in a controlled environment. The results demonstrate the possibility to unmix spectral responses constructed from extremely sparse photon counts (less than 10 photons per pixel and band).

• Research Organization: Univ. of Michigan, Ann Arbor, MI (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0002534; EP/J015180/1; EP/K015338/1; EP/M01326X/1; W911NF-14-1-0479
• OSTI ID: 1367663
• Journal Information: IEEE Transactions on Computational Imaging (CD-ROM), Vol. 3, Issue 4; ISSN 2334-0118
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Multispectral Lidar
Depth imaging
Robust spectral unmixing
Anomaly detection
Markov Chain Monte Carlo