Classification of Active Microwave and Passive Optical Data Based on Bayesian Theory and MRF

Author

Yu, F. ; Li, Hong Tao ; Han, Yunghsiang S. ; Gu, H.Y.

Author_Institution

Chinese Acad. of Surveying & Mapping, Beijing, China

fYear

2011

fDate

9-11 Aug. 2011

Firstpage

1

Lastpage

4

Abstract

A classifier based on Bayesian theory and MRF is presented to classify the active microwave and passive optical remote sensing data, which have demonstrated their respective advantages in inversion of surface soil moisture content. In the method, the VV, VH polarization of ASAR and all the 7 TM bands are taken as the input of the classifier to get the class labels of each pixel of the images. And the model is validated for the necessities of integration of TM and ASAR, it shows that, the total precision of classification in this paper is 89.4%. Comparing with the classification with single TM, the accuracy increase 11.5%, illustrating that synthesis of active and passive optical remote sensing data is efficient and potential in classification.

Keywords

Bayes methods; Markov processes; image classification; remote sensing; synthetic aperture radar; ASAR; Bayesian theory; MRF; Markov random field; active microwave optical data; image classification; passive optical data; remote sensing data; surface soil moisture content; Accuracy; Bayesian methods; Microwave imaging; Optical imaging; Optical polarization; Optical sensors; Remote sensing;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Data Fusion (ISIDF), 2011 International Symposium on

Conference_Location

Tengchong, Yunnan

Print_ISBN

978-1-4577-0967-8

Type

conf

DOI

10.1109/ISIDF.2011.6024265

Filename

6024265