Content-based urban area image retrieval in remote sensing image database
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摘要: 对于大型遥感图像数据库,如何快速准确地检索到需要的图像数据,是一个关键问题。该文在以综合区域匹配算法为图像相似度度量标准的前提下,提出了根据平均高频信号强度降序为排序标准的遥感图像数据库城市区域检索方法。首先,用综合区域匹配算法对图像进行相似度度量;然后,依据这些图像的平均高频信号强度,按降序对这些图像进行重新排序从而得到含有城市区域这一高级语义特征的最终检索结果。实验表明:该方法将检索查准率提高了27%,而且检索效率高,可以满足用户需求。Abstract: How to retrieve the image data quickly and accurately from large remote sensing image database is a critical problem .Using integrated region matching ( IRM) algorithm as image similarity measurement standard , this paper proposes a retrieval approach to retrieve urban area images from remote sensing image database according to Average High Frequency Signal Strength ( AHFSS) values of the stored images , which are used to sort the retrieved images in descending order . The proposed approach firstly utilizes IRM algorithm to measure the similarity measurement of the stored images .Then, the proposed approach resorts the retrieved images in descending order according to AHFSS values of the stored images to obtain the final retrieval result containing high level semantic feature “urban areas”.Experimental results show that the proposed approach increases the retrieval precision by 27%and has reasonable retrieval efficiency to meet users ’ requirements .
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Key words:
- image retrieval /
- urban area /
- image content /
- similarity measurement
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