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摘要: 近年来，天地一体化对地观测系统与智能计算技术的快速发展为遥感科技进步甚至变革提供了难得的机遇。遥感信息技术在历经20世纪60~80年代以统计数学模型为核心的数字信号处理时代、从90年代至今以遥感信息物理量化为标志的定量遥感时代之后，现在正逐渐进入一个以数据模型驱动、大数据智能分析为特征的遥感大数据时代。在总结遥感信息技术历史发展脉络的基础上，阐述了遥感大数据的内涵和智能信息提取的时代特点，并从遥感大数据的理念出发，论述了面向对象的遥感知识库构建，分析了融合遥感知识和深度学习算法的大数据智能信息提取策略。通过典型实例，介绍了以深度学习为代表的智能算法在遥感大数据目标检测、精细分类、参数反演等方面的发展现状与趋势，并讨论了深度学习在遥感大数据时代的智能信息提取方面的应用潜力。Abstract: In recent years, the rapid development of the earth observation capability and the intelligent computing technology has provided opportunities for the advancement and even revolution of remote sensing information technology. Remote sensing data processing technology has experienced the Digi-tal Signal Processing Era from 60s to 80s of last century, which utilizes the Statistical Model as the core, and the Quantitative Remote Sensing Era from 90s marked by the Physical Model. Recently, it is developing towards Remotely Sensed Big Data Era which relies on Data Model by data-driven intelligent analysis. This paper summarizes the history of remote sensing information technology and presents the concept of remotely sensed big data and the characteristics of intelligent information extraction era. Firstly, from the view of remotely sensed big data, this paper discusses the construction of object-based remote sensing knowledge dataset and analyzes the data-driven intelligent information extraction strategy combined the knowledge of remote sensing and deep learning algorithm. Then the current status and development of intelligent algorithms represented by deep learning are introduced by typical applications on object detection, fine classification and parameter inversion based on remote sensing data. Consequently, the application potential of deep learning on intelligent information extraction in Remotely Sensed Big Data Era is discussed.
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Keywords:
- remotely sensed /
- big data /
- deep learning /
- intelligent information extraction /
- neural network
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图 1 融合遥感大数据特征的苍灵系统设计思路
Figure 1. Architecture of the Cangling System Incorporating with the Characteristics of Remotely Sensed Big Data
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图 2 广东省土地利用自动分类结果
Figure 2. Automatic Classification Result of Land Use in Guandong Province
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