CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Semantic Indexing based Remote Sensing Image Retrieval: An Intelligent Decomposition Approach

by Kiran Ashok Bhandari, Manthalkar Ramchandra R
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 19
Year of Publication: 2013
Authors: Kiran Ashok Bhandari, Manthalkar Ramchandra R
10.5120/13000-0004

Kiran Ashok Bhandari, Manthalkar Ramchandra R . Semantic Indexing based Remote Sensing Image Retrieval: An Intelligent Decomposition Approach. International Journal of Computer Applications. 74, 19 ( July 2013), 7-17. DOI=10.5120/13000-0004

@article{ 10.5120/13000-0004,
author = { Kiran Ashok Bhandari, Manthalkar Ramchandra R },
title = { Semantic Indexing based Remote Sensing Image Retrieval: An Intelligent Decomposition Approach },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 19 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number19/13000-0004/ },
doi = { 10.5120/13000-0004 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:42:42.462617+05:30
%A Kiran Ashok Bhandari
%A Manthalkar Ramchandra R
%T Semantic Indexing based Remote Sensing Image Retrieval: An Intelligent Decomposition Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 19
%P 7-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Without formulating physical contact with the object, Remote sensing is the achievement of information about an object or phenomenon. The contact of remote sensing (RS) images will turn into more complicated due to the vast data quantity, to defeat this challenges the users can admittance remote sensing images based on semantics. In the existing method, the Quin-tree is used for the decomposition of Content Based Image Retrieval in Remote Sensing, but it has poor retrieval accuracy. So the intelligent decomposition phase is used in our proposed method, which decomposes the image based on the spatial-spectral heterogeneity. The proposed method will perform visual feature, object semantic, spatial relationship semantic, scene semantic based retrievals to ensure fine retrieval schema, which will obtained by applying mapping and the SS modelling in the decomposed remote sensing image. The human intervention will be introduced in the system to ensure the high retrieval accuracy. The implementation result shows the effectiveness of proposed technique, in segmenting the text lines from the input document. The performance of the proposed method is evaluated by comparing the result of proposed method with the conventional SBRSIR technique. The comparison result shows that our proposed method more accurately retrieves the images based on the VF, OS and SS than the conventional SBRSIR technique.

References
  1. Venkat N. Gudivada I And Vijay V. Raghavan "Modeling And Retrieving Images By Content", Information Processing & Management, Vol. 33, No. 4, Pp. 427-452, 1997.
  2. Ch. Ganapathi Reddy, G. R. Babu and P. V. D. Somasekhar "Image Retrieval By Semantic Indexing", Journal Of Theoretical And Applied Information Technology, Vol. 5, No. 6, Pp. 745-750, 2005.
  3. Wei Wang, Y uqing Song and Aidong Zhang, "Semantics-based Image Retrieval by Region Saliency", Image and Video Retrieval, Vol. 2383, pp. 245-267, 2002.
  4. Zhang Jing ShenLansun, David Dagan Feng, "A Personalized Image Retrieval Based On Visual Perception", Journal of Electronics, Vol. 25 No. 1, pp. 129-133, January 2008.
  5. Julia Vogel, BerntSchiele, "Semantic Modeling of Natural Scenes for Content-Based Image Retrieval", International Journal of Computer Vision, Vol. 72, No. 2, pp. 133-157, 2007.
  6. Ying Liu, Dengsheng Zhang, Guojun Lu, Wei-Ying Ma, "A survey of content-based image retrieval with high-level semantics", Pattern Recognition, Vol. 40, No. 1, pp. 262-282, 2007.
  7. Juan C. Caicedo, Fabio A. Gonzalez and Eduardo Romero, "A Semantic Content-Based Retrieval Method for Histopathology Images", Information Retrieval Technology, Vol. 4993, pp. 51-60, 2008.
  8. Alexander G. Hauptmann, Michael G. Christel and Rong Yan, "Video Retrieval Based on Semantic Concepts", Proceedings of the IEEE, Vol. 96, No. 4, pp. 602-622, April 2008,
  9. Po-Whei Huang and Chu-Hui Lee, "Image Database Design Based on 9D-SPA Representation for Spatial Relations", IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 12, pp. 1486-1496, December 2004.
  10. Yixin Chen, James Z. Wang, and Robert Krovetz, "CLUE: Cluster-Based Retrieval of Images by Unsupervised Learning", IEEE Transactions on Image Processing, Vol. 14, No. 8, pp. 1187-1201, August 2005.
  11. PritiMaheshwary and NamitaSricastava, "Prototype System for Retrieval of Remote Sensing Images based on Color Moment and Gray Level Co-Occurrence Matrix", International Journal of Computer Science Issues, Vol. 3, pp. 20-23, 2009.
  12. Sanjeev S. Sannakki, and Sanjeev P. Kaulgud, "Memory Learning Framework for Retrieval of Neural Objects", International Journal of Advanced Research in Computer Engineering & Technology, Vol. 1, No. 6, pp. 100-106, August 2012.
  13. J. Fournier, M. Cord and S. Philipp-Foliguet "RETIN: A Content-Based Image Indexing and Retrieval System", Pattern Analysis & Applications, Vol. 4, No. 2-3, pp. 153-173, 2001.
  14. Hun-Woo Yoo, Dong-Sik Jang, Seh-Hwan Jung, Jin-Hyung Park and Kwang-Seop Song, "Visual information retrieval system via content-based approach", Pattern Recognition, Vol. 35, No. 3, pp. 749–769, March 2002.
  15. Michael S Ramsey and Luke P Flynn, "Strategies, insights, and the recent advances in volcanic monitoring and mapping with data from NASA's Earth Observing System", Journal of Volcanology and Geothermal Research, Vol. 135, No. 1–2, pp. 1-11, 15 July 2004.
  16. C. Pohl and J. L. Van Genderen, "Multisensory image fusion in remote sensing: concepts, methods and applications", International Journal remote sensing, Vol. 19, No. 5, pp. 823-854, 1998.
  17. PritiMaheshwary and NamitaSrivastava, "Retrieval of Remote Sensing Images Using Color, Texture and Spectral Features", International Journal of Engineering Science and Technology, Vol. 2, No. 9, 4306-4311, 2010.
  18. Sun, Heng, Li, Shixian, Li Wen-jun and Mei Xiaoyong, "Fuzzy Semantic Retrieval of Distributed Remote Sensing Images", International Conference on Computational Intelligence and Security, Vol. 2, pp. 1435 – 1441, 2006.
  19. GaryA. Shaw and Hsiao-hua K. Burke "Spectral Imaging for Remote Sensing", Lincoln Laboratory Journal, Vol. 14, No. 1, pp. 3-28, 2003
  20. FaridMelgani, and Lorenzo Bruzzone "Classification of Hyperspectral Remote Sensing Images with Support Vector Machines", IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 8, pp. 1778-1790, 2004
  21. DevisTuia, Jordi Munoz-Mari and Gustavo Camps-Valls "Remote sensing image segmentation by active queries", Pattern Recognition, Vol. 45, No. 6, pp. 2180–2192, 2012.
  22. Cheng Qiao, JianchengLuo, ZhanfengShen, Zhiwen Zhu and Dongping Ming, "Adaptive thematic object extraction from remote sensing image based on spectral matching", International Journal of Applied Earth Observation and Geoinformation, Vol. 19, pp. 248–251, 2012.
  23. T. Blaschke, "Object based image analysis for remote sensing", ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65, No. 1, pp. 2-16, 2010.
  24. Brandt Tso and Joe L. Tseng, "Multi-resolution semantic-based imagery retrieval using hidden Markov models and decision trees", Expert Systems with Applications, Vol. 37, No. 6, pp. 4425–4434, 2010.
  25. Marco Quartulli and Igor G. Olaizola "A review of EO image information mining", ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 75, pp. 11–28, 2013.
  26. Ruan, Ning; Huang, Ning; Hong, Wen, "Semantic-Based Image Retrieval in Remote Sensing Archive: An Ontology Approach" IEEE International Conference on Geoscience and Remote Sensing Symposium, 2006, Page(s): 2903-2906, 2006
  27. Wang, M. and Song, T. , "Remote Sensing Image Retrieval by Scene Semantic Matching", IEEE Transactions on Geoscience and Remote Sensing, Volume:PP Issue:99, 2012
  28. Stanislav L. Stoev, "RaFSi – A Fast Watershed Algorithm Based on Rainfalling Simulation", In Proceedings of 8-th International Conference on Computer Graphics", 2000
  29. Min Wang, "A Multiresolution Remotely Sensed Image Segmentation Method Combining Rainfalling Watershed Algorithm and Fast Region Merging", rnational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. 37, Beijing 2008.
  30. http://en. wikipedia. org/wiki/Sensitivity_and_specificity
Index Terms

Computer Science
Information Sciences

Keywords

CBIR Remote sensing (RS) images Visual Features (VF) Object Semantics Watershed Segmentation SVM Attribute Relational Graph (ARG) Spatial-spectral Heterogeneity Scene Matching model

Powered by PhDFocusTM