从光谱到时谱——遥感时间序列变化检测研究进展

From Spectrum to Spectrotemporal: Research on Time Series Change Detection of Remote Sensing

  • 摘要: 地物的光谱信息是遥感数据的重要特征,对遥感光谱信息的利用经历了从黑白全色影像到多光谱、高光谱再到时间序列的发展历程。近年来,随着卫星遥感技术的发展和历史数据的积累,大量的重复观测数据被获取。长时序的遥感数据包含光谱维、时间维和空间维4个维度的信息,这在一定程度上能够避免同谱异物、同物异谱的现象。目前还没有统一的概念对长时序遥感数据进行描述,在光谱概念的基础上,提出时谱的概念,详细论述了时谱的理论方法和关键技术,首次尝试对时谱的理论体系进行构建。首先回顾了传统的遥感时间序列变化检测方法,分析传统变化检测方法的优点与不足;然后对时谱的理论方法进行阐述,介绍时谱在数据组织与存储、数据重构、特征深度挖掘和数据源4个方面的关键技术;并介绍基于时谱信息的典型农作物分类和变化检测方法,以期能够为时谱变化检测相关研究提供参考;最后对时谱变化检测研究进行了展望。

     

    Abstract: Spectral information of ground object is an important feature of remote sensing data. The utilization of remote sensing spectral information has experienced the development from black and white panchromatic image to multi-spectral, hyperspectral and time series images. In recent years, with the development of satellite remote sensing and the accumulation of historical data, a large number of revisit observation data have been obtained. The long time series remote sensing data integrate spectral, temporal, and spatial information and can avoid the phenomenon of different object with the same spectra characteristics and same spectrum with different objects to some extent. But there is still no unified concept to describe it. This paper proposed the concept of spectrotemporal based on the concept of spectrum, constructed the theoretical system of spectrotemporal, and discussed the theoretical methods and key technologies of spectrotemporal in detail. First, this paper reviewed the traditional time series change detection methods of remote sensing and analyzed the advantages and disadvantages of the traditional change detection methods. Then, the theory and methods of spectrotemporal were expounded, and the key technologies of spectrotemporal in data organization and storage, data reconstruction, feature depth mining and data source were introduced. The methods of classification and change detection for typical crop based on spectrotemporal information were also proposed. Finally, the research of spectrotemporal change detection is prospected.

     

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