系统梳理遥感数据特征提取方法，助力高效处理海量遥感信息。
Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications
- 构建遥感特征提取框架，贯穿数据价值链
- 推动从单一任务模型向统一表征转变
- 适合遥感、地理信息与机器学习研究者参考
地球观测涉及持续收集、分析和处理海量数据。这些全球性数据对环境监测、城市规划和灾害管理等社会、经济与环境挑战至关重要。然而，其高维特性导致显著的特征冗余和计算开销，限制了机器学习模型的有效性。特征提取（FE）技术通过保留关键数据属性、减少冗余，提升遥感（RS）任务性能。当前遥感特征提取领域方法多样、结构松散且发展迅速。本文提出一个实用的特征提取框架，系统追踪其在遥感数据价值链中的演进历程，并总结趋势，展望未来：一是从单任务模型向统一表征的转变，二是基础模型时代下对鲁棒性与可解释性特征提取的需求，以及经典方法与现代表示学习融合的潜力。
原文摘要 · Abstract (English)
Earth observation involves collecting, analyzing, and processing an ever-growing mass of data. This planetary data is crucial for addressing relevant societal, economic, and environmental challenges, ranging from environmental monitoring to urban planning and disaster management. However, its high dimensionality entails significant feature redundancy and computational overhead, limiting the effectiveness of machine learning models. Feature extraction (FE) techniques address these challenges by preserving essential data properties while reducing redundancy and enhancing tasks in Remote Sensing (RS). The landscape of FE for RS is diverse, disorganized, and rapidly evolving. We offer a practical guide for this landscape by introducing a framework of FE. Using this framework, we trace the evolution of FE across the data value chain in RS. Finally, we synthesize these trends and offer perspectives for the future of FE in RS by first characterizing this shift from single-task models to unified representations, then identifying two perspectives in the foundation model era: the need for robust and interpretable FE and the potential of bridging classical FE with modern representation learning.
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