As the important component of the Earth observation system, hyperspectral imaging satellites provide high-fidelity and enriched information for the formulation of related policies due to the powerful spectral measurement capabilities. However, the transmission speed of the satellite downlink has become a major bottleneck in certain applications, such as disaster monitoring and emergency mapping, which demand a fast response ability. We propose an efficient AI-enabled Satellite Edge Computing paradigm for hyperspectral image classification, facilitating the satellites to attain autonomous decision-making. To accommodate the resource constraints of satellite platforms, the proposed method adopts a lightweight, non-deep learning framework integrated with a few-shot learning strategy. Moreover, onboard processing on satellites could be faced with sensor failure and scan pattern errors, which result in degraded image quality with bad/misaligned pixels and mixed noise. To address these challenges, we develop a novel two-stage pixel-wise label propagation scheme that utilizes only intrinsic spectral features at the single pixel level without the necessity to consider spatial structural information as requested by deep neural networks. In the first stage, initial pixel labels are obtained by propagating selected anchor labels through the constructed anchor-pixel affinity matrix. Subsequently, a top-k pruned sparse graph is generated by directly computing pixel-level similarities. In the second stage, a closed-form solution derived from the sparse graph is employed to replace iterative computations. Furthermore, we developed a rank constraint-based graph clustering algorithm to determine the anchor labels.

翻译：作为地球观测系统的重要组成部分，高光谱成像卫星凭借其强大的光谱测量能力，为相关政策制定提供了高保真且丰富的信息。然而，在某些需要快速响应能力的应用中（如灾害监测与应急测绘），卫星下行链路的传输速度已成为主要瓶颈。本文提出一种面向高光谱图像分类的高效AI赋能卫星边缘计算范式，旨在使卫星具备自主决策能力。为适应卫星平台的资源限制，所提方法采用轻量化的非深度学习框架，并结合了少样本学习策略。此外，卫星上的星载处理可能面临传感器故障与扫描模式误差，导致图像质量退化，出现坏像素/错位像素及混合噪声。为应对这些挑战，我们开发了一种新颖的两阶段像素级标签传播方案，该方案仅利用单像素层面的固有光谱特征，无需像深度神经网络那样考虑空间结构信息。在第一阶段，通过构建的锚点-像素亲和矩阵传播选定的锚点标签，从而获得初始像素标签。随后，通过直接计算像素级相似度生成经top-k剪枝的稀疏图。在第二阶段，采用从稀疏图推导出的闭式解来替代迭代计算。此外，我们还开发了一种基于秩约束的图聚类算法来确定锚点标签。