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Retrieving of remote sensing images based on content-sensitive Bayesian networks and direction relations |
| HU Yuxi1, 2, 3, LI Yikun2, 3, YANG Ping2, 3 |
1. Xi’an Mapping and Printing Company of ARSC, Xi’an 710054, China;
2. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China;
3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China |
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Abstract Retrieving the required remote sensing images effectively and accurately is the kernel of a remote sensing retrieval system. In this paper, the authors proposed a direction based retrieval model based on context-sensitive Bayesian network(CSBN). In addition, an approach was also proposed that is suitable to retrieving urban area images according to the characteristics of urban areas. Initially, the proposed approach retrieved the candidate images based on CSBN. Then, the proposed approach obtained the final retrieval result containing the high level semantic concept “urban area” according to the average high frequency signal strength(AHFSS)of the candidate images. In order to make sure the direction relationships inside the image, the authors used the four directions of northeast, northwest, southeast and southwest to describe eight kinds of directions, which effectively reduced the time complexity of the algorithm. The experimental results show that the proposed approach can effectively describe the semantic concepts of the stored remote sensing images, and thus has higher retrieval precision and efficiency than the original context-sensitive Bayesian network based approach, thus proving that the proposed approach can meet the users’ requirements.
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| Keywords
drought remote sensing monitoring
apparent thermal inertia
anomalies of vegetation index
vegetation supply water index
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Issue Date: 15 August 2017
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2017,
Vol. 29
