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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2512.01788 |
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| _version_ | 1866915647155339264 |
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| author | Mollière, Christian Cumplido, Iker Zeulner, Marco Liesenhoff, Lukas Schubert, Matthias Gottfriedsen, Julia |
| author_facet | Mollière, Christian Cumplido, Iker Zeulner, Marco Liesenhoff, Lukas Schubert, Matthias Gottfriedsen, Julia |
| contents | The rapid growth of data from satellite-based Earth observation (EO) systems poses significant challenges in data transmission and storage. We evaluate the potential of task-specific learned compression algorithms in this context to reduce data volumes while retaining crucial information. In detail, we compare traditional compression (JPEG 2000) versus a learned compression approach (Discretized Mixed Gaussian Likelihood) on three EO segmentation tasks: Fire, cloud, and building detection. Learned compression notably outperforms JPEG 2000 for large-scale, multi-channel optical imagery in both reconstruction quality (PSNR) and segmentation accuracy. However, traditional codecs remain competitive on smaller, single-channel thermal infrared datasets due to limited data and architectural constraints. Additionally, joint end-to-end optimization of compression and segmentation models does not improve performance over standalone optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01788 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learned Image Compression for Earth Observation: Implications for Downstream Segmentation Tasks Mollière, Christian Cumplido, Iker Zeulner, Marco Liesenhoff, Lukas Schubert, Matthias Gottfriedsen, Julia Computer Vision and Pattern Recognition The rapid growth of data from satellite-based Earth observation (EO) systems poses significant challenges in data transmission and storage. We evaluate the potential of task-specific learned compression algorithms in this context to reduce data volumes while retaining crucial information. In detail, we compare traditional compression (JPEG 2000) versus a learned compression approach (Discretized Mixed Gaussian Likelihood) on three EO segmentation tasks: Fire, cloud, and building detection. Learned compression notably outperforms JPEG 2000 for large-scale, multi-channel optical imagery in both reconstruction quality (PSNR) and segmentation accuracy. However, traditional codecs remain competitive on smaller, single-channel thermal infrared datasets due to limited data and architectural constraints. Additionally, joint end-to-end optimization of compression and segmentation models does not improve performance over standalone optimization. |
| title | Learned Image Compression for Earth Observation: Implications for Downstream Segmentation Tasks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.01788 |