Machine learning in remote sensing data processing

Author

Camps-Valls, Gustavo

Author_Institution

Image Process. Lab., Univ. de Valencia, Valencia, Spain

fYear

2009

fDate

1-4 Sept. 2009

Firstpage

1

Lastpage

6

Abstract

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.

Keywords

geophysical signal processing; image classification; learning (artificial intelligence); remote sensing; machine learning; radar images; remote sensing data analysis; remote sensing data processing; remotely sensed multispectral images; signal processing algorithms; Data processing; Economic forecasting; Fires; Floods; Machine learning; Radar detection; Radar imaging; Radar remote sensing; Remote monitoring; Remote sensing;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on

Conference_Location

Grenoble

Print_ISBN

978-1-4244-4947-7

Electronic_ISBN

978-1-4244-4948-4

Type

conf

DOI

10.1109/MLSP.2009.5306233

Filename

5306233