用图模型分析卫星影像时间序列，提升土地覆盖与水资源预测精度
On the use of Graphs for Satellite Image Time Series
- 构建时空图捕捉地物间的空间与时间关系
- 在土地覆盖映射和水资预测任务中表现优于传统方法
- 适合遥感、环境监测领域研究者参考
地球表面经历从板块运动到生态系统、农业及人类活动等复杂动态过程。卫星影像可实现全球范围内的广域时空覆盖，相较于实地观测具有显著优势。特别是卫星影像时间序列（SITS）数据蕴含丰富信息。为应对数据体量大、结构复杂的问题，近年研究转向基于图的方法，摒弃传统的欧几里得网格结构，转而以对象为单位进行建模。图结构能有效刻画对象间的时空交互关系，对模式识别、分类与回归任务至关重要。本文系统探讨图方法在时空遥感分析中的应用，提出一套通用的图构建与下游任务处理流程。重点研究SITS数据生成时空图的方法及其在实际任务中的应用。论文包含全面综述及两个案例研究，分别验证了图方法在土地覆盖制图和水资源预测中的潜力。同时讨论当前局限性与未来发展方向。
原文摘要 · Abstract (English)
The Earth's surface is subject to complex and dynamic processes, ranging from large-scale phenomena such as tectonic plate movements to localized changes associated with ecosystems, agriculture, or human activity. Satellite images enable global monitoring of these processes with extensive spatial and temporal coverage, offering advantages over in-situ methods. In particular, resulting satellite image time series (SITS) datasets contain valuable information. To handle their large volume and complexity, some recent works focus on the use of graph-based techniques that abandon the regular Euclidean structure of satellite data to work at an object level. Besides, graphs enable modelling spatial and temporal interactions between identified objects, which are crucial for pattern detection, classification and regression tasks. This paper is an effort to examine the integration of graph-based methods in spatio-temporal remote-sensing analysis. In particular, it aims to present a versatile graph-based pipeline to tackle SITS analysis. It focuses on the construction of spatio-temporal graphs from SITS and their application to downstream tasks. The paper includes a comprehensive review and two case studies, which highlight the potential of graph-based approaches for land cover mapping and water resource forecasting. It also discusses numerous perspectives to resolve current limitations and encourage future developments.
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