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The combination of Golomb–Rice coding with run-length encoding and context-adaptive binary arithmetic coding within the developed block-splitting framework provides a median compression ratio above 24 for HyperHeight LiDAR data. The proposed methods provide lossless reconstruction and outperform standard NPZ and bit packing when compressing HyperHeight LiDAR data.
The proposed approach enables efficient, lossless onboard compression of large LiDAR datasets for satellite missions such as NASA CASALS. Scalability and data transfer efficiency are improved, supporting real-time environmental monitoring and analysis. Satellite Light Detection and Ranging (LiDAR) systems produce high-resolution data essential for confronting critical environmental challenges like climate change, disaster management, and ecological conservation. A HyperHeight Data Cube (HHDC) is a novel representation of LiDAR data. HHDCs are structured three-dimensional tensors, where each cell captures the number of photons detected at specific spatial and height coordinates. These data structures preserve the detailed vertical and horizontal information essential for ecological and topographical analyses, particularly Digital Terrain Models and canopy height profiles. In this paper, we investigate lossless compression techniques for large volumes of HHDCs to alleviate constraints on onboard storage, processing resources, and downlink bandwidth. We analyze several methods, including bit packing, Rice coding (RC), run-length encoding (RLE), and context-adaptive binary arithmetic coding (CABAC), as well as their combinations. We introduce the block-splitting framework, which is a simplified version of octrees. The combination of RC with RLE and CABAC within this framework achieves a median compression ratio greater than 24, which is confirmed by the results of processing two large sets of HHDCs simulated using the Smithsonian Environmental Research Center NEON data.
Details
Climate change;
Terrain models;
Data transfer (computers);
Environmental monitoring;
Research facilities;
Height;
Datasets;
Mathematical analysis;
Environmental research;
Splitting;
Binary codes;
Lidar;
Disaster management;
Neon;
Emergency preparedness;
Compression ratio;
Entropy;
Data compression;
Photons;
Remote sensing;
Data structures;
Ice sheets;
Tensors;
Arithmetic coding;
Algorithms;
Compression;
Real time;
Data transmission;
Octrees
; Ramirez-Jaime, Andres 2
; Porras-Diaz, Nestor 2
; Vasilyeva Irina 1
; Lukin, Vladimir 1
; Arce Gonzalo 2
; Okarma Krzysztof 3
1 Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkiv, Ukraine; [email protected] (V.M.); [email protected] (I.V.)
2 Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA; [email protected] (A.R.-J.); [email protected] (N.P.-D.); [email protected] (G.A.)
3 Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland; [email protected]