Remote sensing image retrieval based on tolerance granular computing theory
YANG Ping1,2, LI Yikun1,2, HU Yuxi3, YANG Shuwen1,2,4
1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; 2. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China; 3. Xi’an Mapping and Printing Company of ARSC, Xi’an 710054, China; 4. Gansu Province Key Laboratory of Remote Sensing, Lanzhou 730000, China
Abstract：In order to improve efficiency and accuracy of remote sensing image retrieval, this paper proposes a remote sensing image retrieval approach based on granular computing model. Firstly, according to the tolerance granular computing theory, a series of concepts are defined, such as region tolerance granule, image tolerance granule and regional tolerance granular information table, and remote sensing images are granulated. Secondly, the region tolerance granular similarity is calculated. Finally, the remote sensing image similarity model is built combining tolerance granular computing and image integrated region matching algorithm. Using IKONOS data, the authors verified the two retrieval algorithms. The experimental results show that the precision of proposed approach is increased by 12.08% in comparison with original integrated region matching algorithm. Therefore, it can be concluded that the proposed approach can meet the users’ requirements.
杨萍, 李轶鲲, 胡玉玺, 杨树文. 融合相容粒理论的遥感图像检索[J]. 国土资源遥感, 2017, 29(4): 43-47.
YANG Ping, LI Yikun, HU Yuxi, YANG Shuwen. Remote sensing image retrieval based on tolerance granular computing theory. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 43-47.
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2017, Vol. 29 
