期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2014
卷号：XL-6
页码：83-88
DOI：10.5194/isprsarchives-XL-6-83-2014
出版社：Copernicus Publications
摘要：Low-level features tend to achieve unsatisfactory retrieval results in remote sensing image retrieval community because of the existence of semantic gap. In order to improve retrieval precision, visual attention model is used to extract salient objects from image according to their saliency. Then color and texture features are extracted from salient objects and regarded as feature vectors for image retrieval. Experimental results demonstrate that our method improves retrieval results and obtains higher precision
