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14 May 2012 Noise filtering techniques for photon-counting ladar data
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Many of the recent small, low power ladar systems provide detection sensitivities on the photon(s) level for altimetry applications. These "photon-counting" instruments, many times, are the operational solution to high altitude or space based platforms where low signal strength and size limitations must be accommodated. Despite the many existing algorithms for lidar data product generation, there remains a void in techniques available for handling the increased noise level in the photon-counting measurements as the larger analog systems do not exhibit such low SNR. Solar background noise poses a significant challenge to accurately extract surface features from the data. Thus, filtering is required prior to implementation of other post-processing efforts. This paper presents several methodologies for noise filtering photoncounting data. Techniques include modified Canny Edge Detection, PDF-based signal extraction, and localized statistical analysis. The Canny Edge detection identifies features in a rasterized data product using a Gaussian filter and gradient calculation to extract signal photons. PDF-based analysis matches local probability density functions with the aggregate, thereby extracting probable signal points. The localized statistical method assigns thresholding values based on a weighted local mean of angular variances. These approaches have demonstrated the ability to remove noise and subsequently provide accurate surface (ground/canopy) determination. The results presented here are based on analysis of multiple data sets acquired with the high altitude NASA MABEL system and photon-counting data supplied by Sigma Space Inc. configured to simulate the NASA upcoming ICESat-2 mission instrument expected data product.
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Lori A. Magruder, Michael E. Wharton III, Kevin D. Stout, and Amy L. Neuenschwander "Noise filtering techniques for photon-counting ladar data", Proc. SPIE 8379, Laser Radar Technology and Applications XVII, 83790Q (14 May 2012);


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