Understanding satellite images:a data mining module for Sentinel images

作     者：Corneliu Octavian Dumitru Gottfried Schwarz Anna Pulak-Siwiec Bartosz Kulawik Mohanad Albughdadi Jose Lorenzo Mihai Datcu Corneliu Octavian Dumitru;Gottfried Schwarz;Anna Pulak-Siwiec;Bartosz Kulawik;Mohanad Albughdadi;Jose Lorenzo;Mihai Datcu

作者机构：Remote Sensing Technology Institute(IMF)Earth Observation Center(EOC)German Aerospace Center(DLR)WeβingGermany SmallGISKrakowPoland TerranisRamonville France ATOS SPAIN SAMadridSpain 

出 版 物：《Big Earth Data》 (地球大数据（英文）)

年 卷 期：2020年第4卷第4期

页      面：367-408页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：The work was supported by the European Commission’s H2020 CANDELA project under Grant Agreement No.776193 

主　　题：Data mining Earth observation Sentinel-1 Sentinel-2 image semantics classification maps analytics third party mission data 

摘      要：The increased number of free and open Sentinel satellite images has led to new applications of these *** them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by these images,in particular,the identification and quantification of their temporal *** this paper,we will present guidelines and practical examples of how to obtain rapid and reliable image patch labelling results and their validation based on data mining techniques for detecting these temporal changes,and presenting these as classification maps and/or statistical *** represents a new systematic validation approach for semantic image content *** will focus on a number of different scenarios proposed by the user community using Sentinel *** a large number of potential use cases,we selected three main cases,namely forest monitoring,flood monitoring,and macro-economics/urban monitoring.