On the Lossless Compression of HyperHeight LiDAR Forested Landscape Data.
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| Title: | On the Lossless Compression of HyperHeight LiDAR Forested Landscape Data. |
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| Authors: | Makarichev, Viktor1 (AUTHOR), Ramirez-Jaime, Andres2 (AUTHOR), Porras-Diaz, Nestor2,3 (AUTHOR), Vasilyeva, Irina1 (AUTHOR), Lukin, Vladimir1,2 (AUTHOR) v.lukin@khai.edu, Arce, Gonzalo2,3 (AUTHOR), Okarma, Krzysztof3 (AUTHOR) |
| Source: | Remote Sensing. Nov2025, Vol. 17 Issue 21, p3588. 20p. |
| Subjects: | Lossless data compression, LIDAR, United States. National Aeronautics & Space Administration, Digital elevation models, Data compression, Run-length encoding, Environmental monitoring, Remote sensing |
| Abstract: | Highlights: What are the main findings? 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. What are the implications of the main findings? 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? 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. What are the implications of the main findings? 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17213588 |
