用学习型压缩提升遥感图像传输效率，兼顾重建与分割效果
Learned Image Compression for Earth Observation: Implications for Downstream Segmentation Tasks
- 采用混合高斯分布的可学习压缩算法替代传统JPEG 2000
- 多通道光学影像下压缩后分割准确率提升显著，PSNR更高
- 适合大规模遥感图像处理，对小规模热红外数据优势不明显
卫星遥感系统数据量快速增长，带来传输与存储挑战。本文评估任务导向的可学习压缩算法在该场景下的潜力，旨在降低数据量同时保留关键信息。具体对比了传统压缩（JPEG 2000）与基于离散混合高斯似然（Discretized Mixed Gaussian Likelihood）的可学习压缩方法，在火情、云层和建筑检测三类遥感分割任务中的表现。结果表明，对于大规模多通道光学影像，可学习压缩在重建质量（PSNR）与分割精度方面均显著优于JPEG 2000；但在小规模单通道热红外数据集上，传统编码器仍具竞争力，受限于数据量与模型架构。此外，压缩与分割模型联合端到端优化并未带来性能提升，独立优化已足够。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。