Generative Diffusion Models for Compressed Sensing of Satellite LiDAR Data: Evaluating Image Quality Metrics in Forest Landscape Reconstruction
| Author(s) | Ramirez-Jaime, Andres | |
| Author(s) | Arce, Gonzalo R. | |
| Author(s) | Porras-Diaz, Nestor | |
| Author(s) | Ieremeiev, Oleg | |
| Author(s) | Rubel, Andrii | |
| Author(s) | Lukin, Vladimir | |
| Author(s) | Kopytek, Mateusz | |
| Author(s) | Lech, Piotr | |
| Author(s) | Fastowicz, Jarosław | |
| Author(s) | Okarma, Krzysztof | |
| Date Accessioned | 2025-05-13T18:38:33Z | |
| Date Available | 2025-05-13T18:38:33Z | |
| Publication Date | 2025-03-29 | |
| Description | This article was originally published in Remote Sensing. The version of record is available at: https://doi.org/10.3390/rs17071215. © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | |
| Abstract | Spaceborne LiDAR systems are crucial for Earth observation but face hardware constraints, thus limiting resolution and data processing. We propose integrating compressed sensing and diffusion generative models to reconstruct high-resolution satellite LiDAR data within the Hyperheight Data Cube (HHDC) framework. Using a randomized illumination pattern in the imaging model, we achieve efficient sampling and compression, reducing the onboard computational load and optimizing data transmission. Diffusion models then reconstruct detailed HHDCs from sparse samples on Earth. To ensure reliability despite lossy compression, we analyze distortion metrics for derived products like Digital Terrain and Canopy Height Models and evaluate the 3D reconstruction accuracy in waveform space. We identify image quality assessment metrics—ADD_GSIM, DSS, HaarPSI, PSIM, SSIM4, CVSSI, MCSD, and MDSI—that strongly correlate with subjective quality in reconstructed forest landscapes. This work advances high-resolution Earth observation by combining efficient data handling with insights into LiDAR imaging fidelity. | |
| Sponsor | This research was funded in part by US National Science Foundation NSF under Grant No. 2404740, Science & Technology Center in Ukraine (STCU) Agreement No. 7116, and National Science Centre, Poland (NCN), Grant no. 2023/05/Y/ST6/00197, within the joint IMPRESS-U project entitled “EAGER IMPRESS-U: Exploratory Research on Generative Compression for Compressive Lidar”. | |
| Citation | Ramirez-Jaime, Andres, Gonzalo R. Arce, Nestor Porras-Diaz, Oleg Ieremeiev, Andrii Rubel, Vladimir Lukin, Mateusz Kopytek, Piotr Lech, Jarosław Fastowicz, and Krzysztof Okarma. 2025. "Generative Diffusion Models for Compressed Sensing of Satellite LiDAR Data: Evaluating Image Quality Metrics in Forest Landscape Reconstruction" Remote Sensing 17, no. 7: 1215. https://doi.org/10.3390/rs17071215 | |
| ISSN | 2072-4292 | |
| URL | https://udspace.udel.edu/handle/19716/36140 | |
| Language | en_US | |
| Publisher | Remote Sensing | |
| dc.rights | Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| Keywords | canopy height model (CHM) | |
| Keywords | compressive sampling | |
| Keywords | digital terrain model (DTM) | |
| Keywords | light detection and ranging (LiDAR) | |
| Keywords | machine learning (ML) | |
| Keywords | image quality assessment (IQA) | |
| Keywords | hyperheight data cube (HHDC) | |
| Title | Generative Diffusion Models for Compressed Sensing of Satellite LiDAR Data: Evaluating Image Quality Metrics in Forest Landscape Reconstruction | |
| Type | Article |
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