KEYWORDS: Submerged target modeling, Photons, LIDAR, Point clouds, Water, Optical simulations, Monte Carlo methods, Target detection, Refraction, 3D modeling
Airborne Lidar Bathymetry measures ocean depth by transmitting 532nm wavelength lasers and recording the return energy and moment of the laser echo, and is an important means of acquiring the topography of coastal zones and shallow seas. Since underwater targets can also reflect the emitted laser, bathymetric lidar holds promise for underwater target detection. However, most of the existing echo simulation models for laser underwater transmission are for full field-of-view (FOV) and flat terrain, but there are few echo simulation models for hovering targets with sizes smaller than the FOV. Therefore, we combined the ray tracing and semi-analytical Montecarlo methods to simulate the laser echo energy of underwater targets. The model combined the speed advantage of the semi-analytical Montecarlo method with the ability to simulate echoes of underwater targets of arbitrary shapes. In order to simulate bathymetric lidar echoes under real operating conditions, the model also included simulations of the scanning mode and surface waves. The trend of the echo energy of an underwater target within the field of view with the deviation distance from the line-of-sight axis was investigated, and the results showed that even if the underwater target is deviated from the main axis, there is still an obvious target echo due to the scattering of the laser in the water, and the echo energy is weakened with the increase of the depth. The waves can make the ideal refraction direction change, which has a great impact on the quality of the scanned point cloud.
KEYWORDS: LIDAR, Data compression, Clouds, Data storage, Image compression, Field programmable gate arrays, Data transmission, Algorithms, Airborne remote sensing
Due to the high detection sensitivity and efficiency, single photon time-of-flight distance ranging Lidar have emerged in rapid, large scale, high resolution, topographic mapping in recent years. We designed an airborne mapping Lidar system based on arrays of Geiger-mode Avalanche Photodiode (GmAPD) detectors capable of detecting a single photon, and the area coverage rates in excess of 122 km2 /hr. The Lidar system uses an eye-safe, low-power fiber laser and a 64×64 pixel GmAPD arrays capable of readout rates in excess of 20 KHz. Although large-pixel-format GmAPD detector array can obtain plenty laser point cloud in one frame, but the requirement of raw data transmission and storage rate would be particular high when the detect repetition frequency reach at 20 KHz or even more. Therefore, we proposed a lossy realtime data compression algorithm which can reduce a half of data transmission and storage rate so that the data can transmission through low bandwidth situation. By analysis the quality of point cloud, this lossy real-time data compression method had been validated works well during our airborne experiment which had carried out in Wuhu city, China. For a single mapping strip, the point cloud had a mean measurement density greater than 110 points per square meter in flat topography and 75 points per square meter in rough topography when the aircraft above ground levels (AGLs) was 1 km and velocity was 220 km/h.
Single-photon counting Lidar (SPL) systems using Geiger mode Avalanche Photo Diode (Gm-APD) arrays are more sensitive than traditional linear mode Lidar systems and are capable to detect the sparse target-returned photon less than single photon. However the single-photon sensitivity of SPL system also make it susceptible to solar background light which is a major noise source of SPL data. The relatively high noise level of SPL systems poses a significant challenges to the noise filter processing of measured data. In this paper, two image based noise filtering methods: K-Nearest- Neighbor (KNN) filtering method and Single Frame Histogram (SFH) filtering method were proposed, to reduce the noise points in Gm-APD array Lidar data. In these methods, noise points were removed through raw image data processing. We count the number of corresponding time of flight data points in single frame image and remove the noise points from signal through a predefined threshold. The noise filtering results of the two proposed methods were analyzed and compared based on raw data obtained from our 64×64 Gm-APD array Lidar imaging experiment. The noise filtered image results show that more than 90% of the noise points in single frame data has been removed. Finally, the noise filtered image data was further processed to get the cleaned 3D images. The results indicates that the proposed imagebase noise filtering methods is suitable for the noise reduction processing of our (Gm-APD) array Lidar data.
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