MACHINE LEARNING IN REMOTE SENSING DATA PROCESSING

被引：0
|
作者：
Camps-Valls, Gustavo [1 ]
机构：
[1] Univ Valencia, Image Proc Lab IPL, Valencia, Spain
来源：
2009 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING | 2009年
关键词：
HYPERSPECTRAL DATA; LAND-COVER; FEATURE-SELECTION; SEMISUPERVISED CLASSIFICATION; AUTOMATIC CLASSIFICATION; PIXEL CLASSIFICATION; IMAGE CLASSIFICATION; NEURAL-NETWORK; TIME-SERIES; SVM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Remote sensing data processing deals with real-life applications with great societal values. For instance urban monitoring, fire detection or flood prediction from remotely sensed multispectral or radar images have a great impact on economical and environmental issues. To treat efficiently the acquired data and provide accurate products, remote sensing has evolved into a multidisciplinary field, where machine learning and signal processing algorithms play an important role nowadays. This paper serves as a survey of methods and applications, and reviews the latest methodological advances in machine learning for remote sensing data analysis.
引用
收藏
页码：216 / 221
页数：6
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