ELMZip：基于极限学习机的星载图像压缩以实现高效下行传输
ELMZip: Onboard Satellite Image Compression via Extreme Learning Machines for Efficient Downlink
AI总结：
本文提出基于极限学习机（ELM）的星载图像压缩框架ELMZip，通过凸最小二乘问题拟合与非对称传输协议，在高重建保真下实现高效压缩，提升小型卫星下行传输效率，助力实时地球观测。
AI中文摘要：
小型卫星（如立方星）获取多光谱图像时，因数据量大且通信窗口受限，面临严峻的数据下行传输挑战。星载图像压缩是解决该瓶颈的关键，但传统方法常难以适配多波段、多分辨率数据的非线性统计特性。为克服这些局限，本文提出ELMZip，一种基于极限学习机（Extreme Learning Machines, ELM）与域分解策略的新型框架，用于高效、无分辨率限制的星载神经表示。ELMZip将拟合过程表述为基于随机特征单层网络的凸最小二乘问题，从而无需计算成本高昂的反向传播。通过采用仅传输紧凑输出权重的非对称传输协议，该方法大幅降低了下行传输负载。与以往依赖迭代优化且需传输完整网络参数的神经表示方法不同，ELMZip在保持高重建保真度的同时实现了显著的压缩效率。该能力可实现即时图像重建以供分析，使资源受限平台能最大化数据返回，并推进实时AI驱动的地球观测。
英文摘要：
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.
