摘要
特征提取在图像处理中是重要的一环,传统的特征提取算法已无法满足高分辨率图像的要求。研究运用高分辨率SAR图像的词包模型特征提取算法,旨在进一步优化对高分图像的解析。首先通过SIFT算法提取图像关键点,再对关键点进行特征向量提取。在词包模型的特征向量提取中,将边缘算子和WLD描述子作为新的特征向量加入词包模型中,以此提高特征分析对于边缘检测能力以及减少光照差带来的影响。通过对什邡城区SAR图像实测数据的特征提取和分类实验,证明新的词包模型算法具有更好的稳定性和有效性。
Feature extraction is one of the most essential parts in image processing. For traditional algorithms of feature extraction cannot satisfy the high-resolution （HR） images, this paper applies bag-of-word （BOW） model algorithm to op- timize the analysis of HR images feature extraction. First, key points are found by using SIFT algorithm. Second, fea- ture vectors are extracted from key points. In terms of feature extraction of BoW model, this paper propose the mean ra- tio and Weber Local Descriptor （WLD） as new feature vector to improve the performance of ratio detection and decrease the illumination effect. In the feature extraction experiment using the database of Shifang SAR image, the result indicates that the new BoW model has better robustness and efficiency.
出处
《国外电子测量技术》
2015年第6期62-69,共8页
Foreign Electronic Measurement Technology
关键词
词包模型
特征提取
韦伯局部描述子
高分辨率SAR图像
图像分类
bag-of-word （BOW） feature extraction
weber local descriptor （WLD）
high-resolution SAR image
image classification
