An Effective Bag-of-Visual-Words Framework for SAR Image Classification

摘要：

The difficulty existing in synthetic aperture radar (SAR) image classification is large amounts of unpredictable and inestimable speckle, leading to degradation of the image quality and concealing important objectives of interest. By exploiting an efficient image features extraction technique, bag-of-visual-words (BOV) for its ability of ‘midlevel’ feature representation, and a new developed non-local (NL-) means denosing method suitable for multiplicative speckle, we present a novel and effective BOV framework for SAR image classification. Compared with the other two representative algorithms, the experimental results show that the proposed algorithm has obtained more satisfactory and cogent classification performance and performed more robustness to SAR speckle.

关键词： Bag-of-Visual-Words non-local means patch-similarity measurement speckle insensitiveness SAR classification

作者: Jie Feng L. C. Jiao Xiangrong Zhang Ruican Niu

作者单位: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education ofChina, X

会议类型: 国际会议

会议名称: 第七届多光谱图象处理与模式识别国际学术会议

会议地点: 桂林

会议语种:英文

页码: 1-5

在线出版日期: 2011-11-01（万方平台首次上网日期，不代表论文的发表时间）