Content-based urban area image retrieval in remote sensing image database
HU Yuxi1,2, LI Yikun1,2, YANG Shuwen1,2, YANG Ping1,2, YONG Wanling1,2
1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China;
2. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
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.
胡玉玺, 李轶鲲, 杨树文, 杨萍, 雍万铃. 基于内容的遥感图像数据库城市区域检索[J]. 国土资源遥感, 2016, 28(3): 67-72.
HU Yuxi, LI Yikun, YANG Shuwen, YANG Ping, YONG Wanling. Content-based urban area image retrieval in remote sensing image database. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 67-72.
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2016, Vol. 28 
