High resolution Remote Sensing Image retrieval Based on Multi-visual Feature and K-centroid Clustering

  • yanfei Peng yanfei Peng
Keywords: multi-vision features remote sensing image, image retrieval, iterative, clustering


I At present , Resolution remote Sensing image retrieval based on single content has the problem of
one-sided description and imprecise information. The color, shape and Texture features of remote sensing images
were fully used and combined to form multi-vision remote sensing image retrieval in order to solve this
problem. Through a series of iterative operations, the best proportionality coefficient for this three features to
treat Types of remote sensing images can be obtained, which gets a better search result. Aiming at the problem of
the retrieval speed are slow when searching the large image databasefor the color <b1 6>, shape andTexture
features of the remote sensing image respectively , the improved k-centroid clustering algorithm which firstly
clustered the images in the remote Sens ing image database is introduced to reduce the retrieval scope as as the
improve the retrieval speed. The experimental results show that this method has the retrieval results.


Zhang Nan . Research on the key technology of content-based optical Remote Sensing image

retrieval [D]. Long sand : University of Science and Technology , 2008:1-2. ZHANG N. On the content-based retrieval of optical remote sensing image technique

[D]. Changsha: National University of Defense Technology, 2008:1-2. 2. hu Hua Long , Wu Bing , Qinzhiyuan , , and so on . High Resolution remote sensing image woodland boundary

extraction method 0 ]. Journal of Surveying and Mapping science and technology , 2016,33 (4): 394-399. HU H L, WU B, QIN Z Y, et al Forest boundary extraction method applied to high resolution remote sensing images

J]. Journal of Geomatics Science and Technology, 2016, 33 (4): 394-399. 3. dahane G M, Vishwakarma S. Content based image retrieval system [Q. International Journal of Engineering and

innovative Technology, 2012,1 (5): 92-96. 4. ZHAO T Z, LU J J, ZHANG Y F, et al. Feature selection based on genetic algorithm for CBIR [C] ^ IEEE

Congress on Image and Signal processing. Sanya, 2008:495-499. 5. YAO C H, CHEN S Y retrieval of translated, rotated and scaled color textures J. Pattern Recognition, 2003,36 (4):

-929. 6. FAN W G, PRAVEEN P, ZHOU M. genetic-based approaches in ranking function discovery and Optimiz ation in

information retrieval a framework J]. Decision Support Systems, 2009, (4) : 398-407. 7. tores R S, Falcao a X, Goncalves M, et al. A Genetic programming framework for content-based image retrieval

[J]. Journal of the American Society for Information Science and Technology, 2009,42 (2): 283-292. 8. Peng , lijia . based on genetic algorithms and SVM Remote Sensing Image retrieval system for

J. Small computer system , 2016, 4 : 875-880. PENG Y F, LI J. Remote Sensing Image retrieval based on SVM and genetic algorithm [J]. Journal of Chinese

Computer Systems, 2016,37 (4): 875-880. 9. Xu Junfeng, Zhang Paoming, Guo Haitao, etc. . _ Multiple object-oriented for multi-feature fusion Source Remote

Sensing image change detection method J. Journal of Surveying and Mapping science and technology , 015,2

(5) : 505-509. XU J F, ZHANG B M, GUO H T, et al object-oriented change detection for multi-source images using multi-feature

Fusion J . Journal of Geomatics Science and Technology, 2015, 32 (5): 505-509. 10. Triven to, Shaohong . automatic weighting of feature weights in image retrieval based on genetic

algorithm integer J. Computer Engineering and application , 2008 , 2 : 106-108. CUI W C, SHAO H. Automatic feature weight assignment on based genetic for image algorithm J]. Computer

| Remote Sensing

Engineering and Applications, 2008,44 (2): 106-108. 11. Zhang Yongku , Li Yunfeng , Sun Jinguang . Image retrieval based on multi-feature efficient indexing J. Computer

Engineering and application , 2016 , 7 : 181-185. ZHANG y K, LI y F, SUN J. Image retrieval based on efficient index of Multi-features J]. Computer Engineering and

Applications, 2016,52 (7): 181-185. 12. Chahooki M A Z, Charkari N m. Shape retrieval based on manifold learning by fusion of dissimilarity measures

J] . IET Image Processing, 6 (4): 327-336. 13. Kherfi M L, Ziou D. Relevance feedback for cbir:a new approach based on probabilistic feature weighting with

P Ositive and negative examples J]. IEEE transactions on Image Processing, 2006,15 (4): 1017-1030. 14. Lu Minglei, Liu, Zeng Zhiyong. . a Improved K-means of the clustering algorithm image retrieval

algorithm J. Computer Science , 2013 , (8) : 285-288. Lyu m L, LIU D m, ZENG Z Y. Novel image retrieval method of improved K-means clustering algorithm J]. Computer

Science, 2013,40 (8): 285-288. 15. Yi Y, SHAWN N. Bag-of-visual-words and spatial extensions for land-use classification [C] // ACM sigspatial

International Conference on Advances in Geographic Information Systems (ACM GIS). San Jose, USA, 2010:270-279. Editor Chen Shiqing