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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 |
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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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| Keywords
GDP
night light data
land use
spatial
poverty region
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Issue Date: 01 July 2016
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| [1] Zhang B.Intelligent remote sensing satellite system[J].Journal of Remote Sensing,2011,15(3):415-431.
[2] Li Y K.Semantic-Sensitive Remote Sensing Imagery Retrieval[M].Beijing:China Environmental Press,2014:1-7.
[3] Li Y K,Yang S W,Liu T,et al.Comparative assessment of semantic-sensitive satellite image retrieval:Simple and context-sensitive Bayesian networks[J].International Journal of Geographical Information Science,2012,26(2):247-263.
[4] Wang J,Li J,Wiederhold G.SIMPLIcity:Semantics-sensitive integrated matching for picture libraries[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(9):947-963.
[5] Tian Y M,Wu Z L,Meng L N.A region-interactive retrieval model based on IRM algorithm[C]//Proceedings of the 20055th International Conference on Information Communications and Signal Processing.Bangkok:IEEE,2005:692-695.
[6] Zakariya S M,Ali R,Ahmad N.Combining visual features of an image at different precision value of unsupervised content based image retrieval[C]//Proceedings of 2010 IEEE International Conference on Computational Intelligence and Computing Research(ICCIC).Coimbatore:IEEE,2010:1-4.
[7] Wang M,Song T.Remote sensing image retrieval by scene semantic matching[J].IEEE Transactions on Geoscience and Remote Sensing,2013,51(5):2874-2886.
[8] Zhuang D W,Wang S J.Content-based image retrieval based on integrating region segmentation and relevance feedback[C]//Proceedings of 2010 International Conference on Multimedia Technology (ICMT).Ningbo:IEEE,2010:1-3.
[9] 李轶鲲,闫浩文,孙建国.分步式卫星图像检索[J].测绘科学,2009,34(6):53-55. Li Y K,Yan H W,Sun J G.Stepwise satellite image retrieval[J].Science of Surveying and Mapping,2009,34(6):53-55.
[10] 夏定元,付翩,刘丽端.一种综合区域匹配的图像检索改进算法[J].计算机工程与应用,2012,48(26):197-200. Xia D Y,Fu P,Liu L D.Improved image retrieval algorithm for integrated region matching[J].Computer Engineering and Applications,2012,48(26):197-200.
[11] 郭媛,毛琦,陈小天,等.干涉条纹快速加窗傅里叶滤波方法的研究[J].光学学报,2014,34(6):151-155. Guo Y,Mao Q,Chen X T,et al.Study of a fast windowed Fourier filtering method for interference fringes[J].Acta Optica Sinica,2014,34(6):151-155.
[12] 李杰.基于内容的图像检索方法研究[D].合肥:中国科学技术大学,2008. Li J.Research on Content Based Image Retrieval[D].Hefei:University of Science and Technology of China,2008. |
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2016,
Vol. 28
