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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