摘要
利用尺度不变特征变换(scale invariant feature transform,SIFT)算子直接提取遥感影像局部特征进行检索时存在关键点数目多、特征维数高等问题,因此,本文利用视觉注意模型,根据目标显著性的大小从影像上提取显著目标区域,并采用K-means聚类方法对提取的SIFT局部特征进行聚类,得到用于检索的特征向量。实验结果表明,该方法不仅符合人眼的视觉特性,且在降低SIFT关键点数目和特征维数的同时提高了检索精度和检索效率。
SIFT descriptor is widely used for local feature extraction. However, some problems such as large numbers of extracted key points and its high dimension appear when using SIFT to extract local features from remote sensing imagery directly. To solve these prob ems and improve the retrieval results, we use a visual attention model to extract objects using their saliency from remote sensing images. The visual attention model is used to extract salient objects through their saliency from remote sensing images firstly, then we use a K-means algorithm to cluster local features, these results are then used as feature vectors for similarity measures. Some experimental results show that our method not only decreases the number of key points and the dimension of local features, but also improves retrieval results at the same time. It also accords with the human visual system.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2015年第1期46-52,共7页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金资助项目(61172174)
教育部新世纪优秀人才基金资助项目(NCET-12-0426)
湖北省自然科学基金杰青资助项目(2013CFA024)~~
