For providing a feasible solution for real-time terrain classification using ICESat-2 data, a supervised machine-learning-based real-time terrain classification method using ICESat data is proposed, which is composed of two procedures: offline classifier training and online terrain classification. The first procedure is accomplished by true label generation and feature selection. We advanced a new method of true label generation that uses laser-footprint-scale slopes and waveform characteristics as input data. The laser-footprint-scale slopes are estimated by an improved slope estimation method using elevations of repeat tracks. For feature selection, several waveform characteristics are extracted and the best ones are selected based on experiments. Using the generated true labels and selected features, we train a support vector machine classifier for the second procedure and the performance of online terrain classification is tested using ICESat/GLAS data collected from Beijing area during 2003-2009. The results show that by carefully selecting the features, high classification accuracy (around 92.36%) can be achieved even if the feature dimension is significantly reduced. Furthermore, the classification time of one laser footprint, which is approximately equal to 0.006 s, is less than one laser shot time; thus, the proposed method can be used for real-time terrain classification.

Empirical models are proposed for recalculating non-Lambertian reflectance based on the transmitted pulses and returned pulses that are recorded by a lab-built full-waveform laser detection system. The experiments were implemented on three objects, which were gray-cement concrete, red dull paper, and glazed indoor tile. The pulse energy was calculated based on the pulse waveforms in two ways, integral of the waveform (IW) method and multiplying peak by the full width at half maximum of the waveform (PF) method. The newly introduced empirical parameters semi-ellipsoid distribution ratio (SEDR) of the semi-ellipsoid model and ellipsoid distribution ratio (EDR) of the ellipsoid model were put forward to evaluate the degree of the non-Lambertian reflectance of material surface, instead of Lambertian-based factor that is the cosine of the incidence angle. We conclude that the bigger values of SEDR and EDR indicate more significant deviation from Lambertian for material surface. The modified reflectance results estimated by using the semi-ellipsoid model display better approximations than those obtained from Phong cosine. Moreover, it is obvious that the modified reflectance using a combined method of PF and modified semi-ellipsoid model outweighs the results estimated by other manners.

We present theoretical analysis and experimental verification of the signal to noise ratio (SNR) of a common-path interferometer-based optical coherence tomography (OCT) system. Based on fully integrated all-fiber implementation of a common-path time-domain OCT system, we derived the SNR of the system including the effect of beat noise, which turns out to be twice as large as the excess noise term. We verified the theoretical SNR through a series of experiments, utilizing both controlled phantom and biological samples such as a rat brain with tumor and a frog retina. The results showed that the source power and the reference reflectivity can be easily controlled to optimize the SNR of OCT imaging. We have also analyzed the effect of the fiber delays and the offset in the fiber autocorrelator of the common-path OCT system on the overall SNR. (C) 2008 Optical Society of America