用极限学习机实现卫星图像高效压缩，省带宽还保画质。
ELMZip: Onboard Satellite Image Compression via Extreme Learning Machines for Efficient Downlink
- 基于随机特征的单层网络，免反向传播，计算快。
- 仅传输紧凑权重，下行数据量大幅减少。
- 适合资源受限的微型卫星实时图像处理。
小型卫星（如立方星）获取多光谱影像面临数据量大、通信窗口受限的下行挑战。传统在轨压缩方法难以适应多波段、多分辨率数据的非线性统计特性。为此，我们提出ELMZip框架，基于极限学习机（ELM）与领域分解策略，实现无分辨率限制的高效神经表征。该方法将拟合过程建模为凸最小二乘问题，利用随机特征的单层网络，避免耗时的反向传播。通过采用不对称传输协议，仅发送紧凑的输出权重，显著降低下行负载。相比依赖迭代优化且需传输完整网络参数的前序神经表征方法，ELMZip在保持高重建保真度的同时实现显著压缩效率。该能力支持即时图像重构分析，使资源受限平台最大化数据回传，推动实时人工智能地球观测发展。
原文摘要 · Abstract (English)
The acquisition of multispectral imagery via small satellites (e.g., CubeSats) presents significant data downlink challenges due to high data volumes and restricted communication windows. While onboard image compression is critical to address this bottleneck, traditional methods often struggle to adapt to the nonlinear statistics of multi-band, multi-resolution data. To overcome these limitations, we propose ELMZip, a novel framework based on Extreme Learning Machines (ELM) and domain decomposition strategies for efficient, resolution-free onboard neural representation. ELMZip formulates the fitting process as a convex least-squares problem using random-feature single-layer networks, thereby eliminating the need for computationally expensive backpropagation. By adopting an asymmetric transmission protocol that sends only the compact output weights, the proposed method significantly reduces the downlink payload. Unlike previous neural representation approaches that rely on iterative optimization and require transmitting full network parameters, ELMZip achieves significant compression efficiency while maintaining high reconstruction fidelity. This capability enables immediate image reconstruction for analysis, allowing resource-constrained platforms to maximize data return and advancing real-time AI-powered Earth observation.
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