Title :
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
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;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4577-1003-2
DOI :
10.1109/IGARSS.2011.6050060
