CFP last date
20 January 2025
Reseach Article

Development of Nanosatellite based Image Retrieval system

by Jean Marie Gashayija, Almarie Bierman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 11
Year of Publication: 2014
Authors: Jean Marie Gashayija, Almarie Bierman
10.5120/17418-8208

Jean Marie Gashayija, Almarie Bierman . Development of Nanosatellite based Image Retrieval system. International Journal of Computer Applications. 99, 11 ( August 2014), 25-31. DOI=10.5120/17418-8208

@article{ 10.5120/17418-8208,
author = { Jean Marie Gashayija, Almarie Bierman },
title = { Development of Nanosatellite based Image Retrieval system },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 11 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number11/17418-8208/ },
doi = { 10.5120/17418-8208 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:27:56.558841+05:30
%A Jean Marie Gashayija
%A Almarie Bierman
%T Development of Nanosatellite based Image Retrieval system
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 11
%P 25-31
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rapid development of nanosatellites has led to many unique and innovative space applications. These tiny satellites have come a long way since Sputnik, the first satellite that was launched in 1957, weighing 83 kg. The success of the CubeSat has revolutionized space technology. Typically, these CubeSat fall into categories of nanosatellites weighing no more than 1. 33 Kg and using less power than a five watt light bulb. Mostly, CubeSat has one or more payloads such as Imaging Payload, Scientific payload and High Frequency (HF) Radio Beacon. With the Imaging payload their task is to capture low or high resolution images of the earth observation missions. With the steadily increasing demand for CubeSat imaging payload missions, several nano-satellites have been launched, and thousands of low/high resolution images are acquired every day and transmitted to ground stations. This leads to an exponential increase in the number of low/high resolution images in databases. Therefore, how to retrieve useful images quickly and accurately from a huge and unstructured image database becomes a challenge. In this project, it proposes the use of a content-based image retrieval (CBIR) system relying on a combination of the three low level features such as color, shape and texture features. In order to accurately classify and retrieve useful information in a huge image database. The evaluation performance of the proposed system when compared to other existing systems provides a precision value of 1 or 100%. It can be concluded that the proposed system is able to classify images and retrieve results in a reasonable time of at least 15 seconds.

References
  1. Afifi, A. J. 2011. Image retrieval based on content using color feature. M. Tech Thesis, Islamic University of Gaza.
  2. Asubam, W. B. 2011. Improved median filtering algorithm for the reduction of impulse noise in corrupted 2D greyscales images. MSc Thesis, Kwame Nkrumah University of Science and Technology.
  3. Chaudhari, R. , & Patil, A. M. 2012. Content based image retrieval using color and shape features. International Journal of Advanced using color and shape features,1(5):386-392,November.
  4. Chun-Yu, N. , Shu-Fen, L. & Ming, Q. 2009. Research on removing noise in medical image based on median filter method. Proceedings of the 2009 IEEE symposium on Information Technology(IT) in medicine and Education ,Ji'nan,China,14-16 August 2009.
  5. Das, N. , Pal, A. , Mazumder, S. , Gangopadhyay, D. & Nasipuri, M. 2013. An SVM based skin disease identification using local binary patterns. The 2013 third International Conference on Advances on Computing and Communications,Kochi,Kerala,India, 29-31 August 2013.
  6. Duan, G. , Suzuki, Y. & Kawagoe, K. 2006. Grid Representation for Efficient similarity search in time series databases. Proceedings of the 2006 22nd International Conference on Data Engineering Workshops(ICDEW'06),Atlanta,Georgia,3-7 April 2006.
  7. El-Ghazal, A. , Basir, O. & Belkasim, S. 2009. Farthest point distance: A new shape signature for fourier descriptors. Signal Proceeding Image Communication,24:572-586.
  8. French South Africa Institute of Technology (FSATI). 2014. http://www. cput. ac. za/fsati/[10 June 2014].
  9. Gebril, M. M. 2011. Structural indexing of satellites images. PhD Thesis, North Carolina A & T State University.
  10. Haldar, P. & Mukherjee, J. 2012. Content based image retrieval using histogram,color and edge. International Journal of Computer Applications , 48 (11):25-31.
  11. Jafarpour, S. , Sedghi, Z. & Amirani, M. C. 2012. A robust brain MRI Classification with GLCM features. International Journal of Computer Applications , 37 (12):1-5,January.
  12. Kalapala, M. 2014. Estimation of tree count from satellite imagery through mathematical morphology. International Journal of Advanced Research in Computer Science and Software Engineering,4(1): 490-495,January.
  13. Khurshid, K. , Mahmood, R. & Islam, Q. U. 2013. A survey of camera modules for CubeSats: Design of imaging payload of ICUBE-1. Proceedings of the 6th International Conference on Recent Advances in Space Technologies,Istanbul,Turkiye,12-14 June 2013.
  14. Kuuste, H. , Eenmae, T. , Allik, V. , Agu, A. , Vendt, R. , Ansko, I. , Laizans, K. , Sunter,I. , Latt, S. & Noorma, M. 2014. Imaging system for nanosatellite proximity operations. Proceedings of the Estonian Academy of Sciences,2014,63,2s,250-257. Available http://www. kirj. ee/public/proceedings_pdf/2014/issue_2S/Proc-2014-2S-250-257. pdf.
  15. Li, B. , Li, G. , Wang, Y. , Wang, X. , & Wang, W. 2007. A classification mehod of pear shape based on zernike moment. Proceedings of he 2007 International Conference on Wavelet Analysis and Pattern Recognition,Beijing,China,2-5 November 2007.
  16. Lin, C. H. , Chen, R. T. , & Chan, Y. K. 2009. A smart content based image retrieval sysem based on color and texture feature. Image and vision computing , 27, 658-665.
  17. Liu, T. , Zhang, L. , Li, P. , & Lin, H. 2012. Remotely sensed image retrieval based on region level semantic mining. EURASIP Journal on Image and Video Processing , 1 (4): 1-11.
  18. Masat-1. 2014. Projects. http://cubesat. bme. hu/en/projektek/masat-1/[10 June 2014].
  19. Mikhraq, A. K. 2013. Content based image retrieval(CBIR) system based on the clustering and genetic algorithm. MSc. Thesis, Islamic University of Gaza.
  20. Mohanaiah, P. , Sathyanarayana, P. , & Gurukumar, L. 2013. Image texture feature extraction using GLCM approach. International Journal of Scientific and Research Publications ,3(5):1-5, May.
  21. Murala, S. , Gonde, A. B. , & Maheshwari, R. P. 2009. Color and texture features for image indexing and retrieval. The 2009 IEEE International Advance Computing Conference(IACC2009), Patiala,India, 6-7 March 2009.
  22. Nunes, J. F. , Moreira, P. M. , & Tavares, J. S. 2010. Shape based image retrieval and classification. The proceeding of the 2010 5th Iberian Conference on Information System and Technologies(CISTI-2010), Hotel Congreso Santiago de Compostela,Spain, 16-19 June 2010.
  23. Raghupathi, G. , Anand, R. S. & Dewal, M. L. 2010. Color and textures features for content based image retrieval. Proceedings of he second International Conference on multimedia and content based image retreval,21-23 July 2010.
  24. Rautiainen, M. & Doermann, D. 2002. Temporal color correlograms for video retrieval. Proceedings of the 16th International Conference on Pattern Recognition,Vol2, Qubec City(QC), Canada,11-15 August 2002.
  25. Regniers, O. , Da Costa, J. P. , Grenier, G. , Germain, C. & Bombrun, L. 2013. Texture based image retrieval and classification of very high resolution maritime pine forest images. IEEE International Geoscience and Remote Sensing Symposium,Melbourne,Australia.
  26. Rizon, M. , Yazid, H. , Saad, P. , Shakaff, A. M. , Saad, A. R. , Mamat, M. R. ,Yaacob,S. ,Desa,H. & Karthigayan,M. 2006. Object detection using geometric invariant moment. America Journal of Applied Sciences , 2 (6):1876-1878.
  27. Roslan, R. , Jamil, N. & Mahmud, R. 2011. Skull stripping magnetic resonance images brain images: Region versus mathematical morphology. International Journal of Computer Information Systems and Industrial Management Applications , 3:150-158.
  28. Safar, M. , Shahabi, C. , & Sun, X. 2000. Image rerieval by shape: A comparative study. Institute of Electrical and Electronic Engineers (IEEE) , 141-144.
  29. Sergyan, S. 2008. Color histogram features based image classification in content based image retrieval systems. Proceedings of the 2008 6th International Symposium of Applied Machine Intelligence and Informatics(SAMI2008),Herlany,Slovakia, 21-22 January 2008.
  30. Shih, J. L. & Chen, L. H. 2002. Color image retrieval based on primitives of color moments. Vision,Image and Signal Processing, IEEE Proceedings , 149(6):370-376.
  31. Sivappriya, T. , & Muthukumaran, K. 2014. Cancer cell detection using mathematical Morphology. International Journal of Innovative Research in Compuer and Communication Engineering , 2 (1):3717-3725,March.
  32. Suhasini, P. S. , Krishna, K. R. & Krishna, I. M. 2009. CBIR using color histogram processing. Journal of Theoretical and Applied Information Technology , 6 (1): 116 -122.
  33. Tahmasbi, A. , Saki, F. , Aghapanah, H. & Shokouhi, S. B. 2011. A novel breast mass diagnosis system based on zernike moments as shape and density descriptors. The Proceedings of 18th Iranian Conference on BioMedical Engineering,Tehran,Iran,14-16 December 2011.
  34. Tarabalka, Y. , Fauvel, M. , Chanussot, J. , & Benediktsson, J. A. 2010. SVM and MRF based method for accurate classification of hyperspectral images. IEEE Geoscience and remote sensing letters , 7 (4): 736-740,October.
  35. Vaddi, R. S. , Boggavarapu, L. P. , Vankayalapati, H. D. & Anne, K. R. 2011. Contour detection using freeman chain code and approximation methods for the real time object detection. Asian Journal of Computer Science and Information Technology(AJCSIT),1(1):15-17.
  36. Valero-Lara, P. 2012. MRF Satellite image classification on GPU. The Proceedings of the 2012 41st International Conference on Parallel Processing Workshops(ICPP-2012),Pittsburg, PA,USA, 10-13 September 2012.
  37. Valova, I. , Rachev, B. , & Vassilakopoulos, M. 2006. Optimization of the algorithm for image retrieval by color features. International Conference on Computer Systems and Technologies-CompSysTech , 1-4.
  38. Vansteenkiste, E. , Schoutteet, A. , Gautama, S. , & Phillips, W. 2004. Comparing color and textural information in very high resolution satellite image classification. The Proceeding of the 2004 International Conference on Image Processing(ICIP-2004), Singapore, 24-27 October 2004.
  39. Wang, S. L. , & Liew, W. C. 2007. Information based color feature representation for image classification. The Proceedings of the 2007 14th IEEE International Conference on Image Processing(ICIP 2007),San Antonio,Texas, USA,16-19 Sepetember 2007.
  40. Wang, X. Y. , Yu, Y. J. & Yang, H. Y. 2011. An effective image retrieval scheme using color,texture and shape features. Computer Standards and Interfaces ,33 :59-68,March.
  41. Yasmin, M. , Sharif, M. & Mohsin, S. 2013. Use of low level features for content based image retrieval: Survey. Research Journal of Recent Sciences , 2(11): 65-75,June 2.
  42. Zhang, D. & Lu, G. 2004. Review of shape representation and description techniques. Pattern Recognition , 37:1-19.
  43. Zhang, D. , & Lu, G. 2001. Content based shape retrieval using different shape descriptors: A comparative study. The 2001 IEEE International Conference on Multimedia and Expo(IICME), Tokyo, Japan, 22-25 August 2001.
Index Terms

Computer Science
Information Sciences

Keywords

CubeSat Content Based Retrieval System Color Feature Shape Feature Texture Features and Image Database.