Remotely Sensed Big Data Era and Intelligent Information Extraction

被引：0

作者：

Zhang B. ^{[1
,2
]}

机构：

[1] Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing

[2] University of Chinese Academy of Sciences, Beijing

来源：

Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2018年 / 43卷 / 12期

关键词：

Big data; Deep learning; Intelligent information extraction; Neural network; Remotely sensed;

D O I：

10.13203/j.whugis20180172

中图分类号：

学科分类号：

摘要：

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. © 2018, Research and Development Office of Wuhan University. All right reserved.

引用

页码：1861 / 1871

页数：10

共 64 条

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