A graph-based approach to detect spatiotemporal dynamics in satellite image time series
A graph-based approach to detect spatiotemporal dynamics in satellite image time series
复制标题
一种基于图的卫星图像时间序列时空动态检测方法
DOI：
10.1016/j.isprsjprs.2017.05.013
复制
发表时间：
2017-08
影响因子：
12.7
通讯作者：
Fabio Güttler;D. Ienco;Jordi Nin;M. Teisseire;P. Poncelet
中科院分区：
文献类型：
--
作者：
Fabio Güttler;D. Ienco;Jordi Nin;M. Teisseire;P. Poncelet
提高卫星获取的频率是当今地球观测界的一个关键问题。重复观察对于监测目的至关重要，特别是在应考虑年度内进程的情况下。在这些情况下，图像的时间序列构成了宝贵的信息来源。本文的目标是提出一种新的方法框架，自动检测和提取时空信息的卫星图像时间序列（SITS）。现有的方法处理这类数据通常是面向分类的，不能提供有关演化和时态行为的信息。在本文中，我们提出了一个基于图形的策略，结合基于对象的图像分析（OBIA）与数据挖掘技术。在每个单独的时间戳计算的图像对象在时间序列上连接，并生成一组演化图。每个演变图都与研究地点内的特定区域相关联，并存储有关其时间演变的信息。这些信息可以在进化图尺度上进行深入探讨，也可以用于比较图，并在研究地点尺度上提供一个总体图。我们验证了我们的框架在两个研究地点位于法国南部，涉及不同类型的自然，半自然和农业地区。从陆地卫星SITS获得的结果支持的质量的方法，并说明如何可以利用该框架提取和表征时空动态。
Enhancing the frequency of satellite acquisitions represents a key issue for Earth Observation community nowadays. Repeated observations are crucial for monitoring purposes, particularly when intra-annual process should be taken into account. Time series of images constitute a valuable source of information in these cases. The goal of this paper is to propose a new methodological framework to automatically detect and extract spatiotemporal information from satellite image time series (SITS). Existing methods dealing with such kind of data are usually classification-oriented and cannot provide information about evolutions and temporal behaviors. In this paper we propose a graph-based strategy that combines object-based image analysis (OBIA) with data mining techniques. Image objects computed at each individual timestamp are connected across the time series and generates a set of evolution graphs. Each evolution graph is associated to a particular area within the study site and stores information about its temporal evolution. Such information can be deeply explored at the evolution graph scale or used to compare the graphs and supply a general picture at the study site scale. We validated our framework on two study sites located in the South of France and involving different types of natural, semi-natural and agricultural areas. The results obtained from a Landsat SITS support the quality of the methodological approach and illustrate how the framework can be employed to extract and characterize spatiotemporal dynamics.