Multi-source and multi-temporal remote sensing image classification for flood disaster monitoring  

作　　者：LI Zhu JIA Zhenyang DONG Jing LIU Zhenghong 

机构地区：[1]College of Earth Sciences,Jilin University,Changchun 130061,China [2]Dandong No.2 High School,Dandong 118009,China

出　　处：《Global Geology》2025年第1期48-57,共10页世界地质(英文版)

摘　　要：Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.

关 键 词：MULTI-TEMPORAL decision tree classification flood disaster monitoring 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]