Scene interpretation for SAR images using supervised topic models

Author

Liu, Bin ; Wang, Huanyu ; Wang, Kaizhi ; Liu, Xingzhao ; Yu, Wenxian

Author_Institution

Dept. of Electron. Eng., Shanghai Jiao Tong Univ., Shanghai, China

fYear

2011

fDate

24-29 July 2011

Firstpage

3807

Lastpage

3810

Abstract

In this paper, we present a scene interpretation framework for Synthetic Aperture Radar (SAR) images, using keywords of the image contents provided by users. The framework consists of incorporation of prior knowledge with SAR iMage Annotation Tool (SARMAT), representation of SAR images, and prediction of scene labels based on the supervised Latent Dirichlet Allocation (sLDA) model. The experiment on a TerraSAR-X SAR image shows that the proposed framework provides a promising performance for SAR image scene interpretation.

Keywords

geophysical image processing; image representation; radar imaging; remote sensing by radar; synthetic aperture radar; SAR Image Annotation Tool; SAR image representation; SAR image scene interpretation; SARMAT; TerraSAR-X SAR image; image content; prior knowledge; sLDA model; scene label prediction; supervised latent Dirichlet allocation; supervised topic models; synthetic aperture radar; Feature extraction; Labeling; Probabilistic logic; Resource management; Semantics; Synthetic aperture radar; Training data; SAR images; sLDA model; scene interpretation;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Conference_Location

Vancouver, BC

ISSN

2153-6996

Print_ISBN

978-1-4577-1003-2

Type

conf

DOI

10.1109/IGARSS.2011.6050060

Filename

6050060