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Supervised Techniques and Approaches for Satellite Image Classification

by Minu Nair S., Bindhu J.S.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134 - Number 16
Year of Publication: 2016
Authors: Minu Nair S., Bindhu J.S.
10.5120/ijca2016908202

Minu Nair S., Bindhu J.S. . Supervised Techniques and Approaches for Satellite Image Classification. International Journal of Computer Applications. 134, 16 ( January 2016), 1-6. DOI=10.5120/ijca2016908202

@article{ 10.5120/ijca2016908202,
author = { Minu Nair S., Bindhu J.S. },
title = { Supervised Techniques and Approaches for Satellite Image Classification },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 134 },
number = { 16 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume134/number16/23995-2016908202/ },
doi = { 10.5120/ijca2016908202 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:34:20.865408+05:30
%A Minu Nair S.
%A Bindhu J.S.
%T Supervised Techniques and Approaches for Satellite Image Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 134
%N 16
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Remote Sensing is a multi-disciplinary technique for image acquisition and measurement of information. Remote sensing analysis paved way for satellite image classification which facilitates the image interpretation of large amount of data. Satellite Images covers large geographical span and results in the exploitation of huge information which includes classifying into different sectors. Different classification algorithms exist for image classification, but with the wide range of applications an algorithm with improved performance in terms of accuracy is required. Here in this paper we analyze different methods of supervised classification, different post classification techniques, spectral contextual classification and provide a comparative study on their efficiency.

References
  1. E. C. Barret and L. F. Curtis, Introduction to Environmental Remote Sensing. Cheltenham, U.K.: Cheltenham Stanley Thornes Publishers
  2. Bhabatosh Chanda,Dwijesh Dutta Mjumder, Digital Image processing and analysis.
  3. W. G. Rees, Physical Principles of Remote Sensing, 2nd ed. Cambridge,U.K.: Cambridge Univ. Press, 2001
  4. P. Mather and B. Tso, Classification Methods for Remotely Sensed Data,2nd ed. Boca Raton, FL, USA: CRC Press, 2009.
  5. Sunitha Abburu , Suresh Babu Golla Satellite Image Classification Methods and Techniques: A Review, International journal of computer applications, Volume 119 No.8, June 2015
  6. Pooja Kamavisdar, Sonam Saluja, and Sonu Agrawal, A Survey on Image Classification Approaches and Techniques, in Proc. IJARCCE.
  7. Minakshi Kumar DIGITAL IMAGE PROCESSING Indian Institute of Remote Sensing, Dehra Dun
  8. B.K. Mayanka; Classification of Remote Sensing data using KNN method, Journal Of Information, Knowledge And Research In Electronics And Communication Engineering( Volume 02, Issue 02)
  9. Smriti Sehgal, Remotely Sensed LANDSAT Image Classification Using Neural Network Approaches, International Journal of Engineering Research and Applications, Vol. 2, Issue 5, September- October 2012
  10. Aarsi Saini, Er. Sukvinder Kaur,Er. DV Saini Satellite Image Classification using Artificial Neural Network, SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) EFES April 2015
  11. Priyanka Sharma, Urvashi Mutreja Analysis of Satellite Images using Artificial Neural Network, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-6, January 2013
  12. Moje Ravindra K, Patil Chandrashekhar G Classification of Satellite images based on SVM classifier Using Genetic Algorithm, INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND CONTROL ENGINEERING Vol. 2, Issue 5, May 2014
  13. Nur Anis Mahmon, Norsuzila Yaacob, Azita Laily Yusof, Differences of Image Classification Techniques for Land Use and Land Cover Classification 2015 IEEE 11th International Colloquium on Signal Processing and its Applications (CSPA2015), 6 -8 Mac. 2015, Kuala Lumpur, Malaysia
  14. Ming-Hseng Tseng, Sheng-Jhe Chen, Gwo-Haur Hwang , Ming-Yu Shen d A genetic algorithm rule-based approach for land-cover classification, ISPRS Journal of Photogrammetry and Remote Sensing 63 (2008) 202212, October 2007
  15. E. Sarhan, E. Khalifa, and A. M. Nabil, Post classification using cellular automata for Landsat images in developing countries, in Proc. ICIIP
  16. A. Popovici and D. Popovici, Cellular automata in image processing, in Proc. 15th Int. Symp. Math. Theory Netw. Syst., 2002, pp. 16.
  17. M. Espinola et al., ”Classification of satellite images using the cellular automata approach in Proc. 1st WSKS, vol. 19, CCIS,” 2008, pp. 521526
  18. M. Espnola et al., Cellular automata applied in remote sensing to implement contextual pseudo-fuzzy classification, in Proc. 9th Int. Conf. ACRI, vol. 6350, Lecture Notes in Computer Science, 2010, pp. 312321
  19. ERIK MOHN NILS L. HJORT, AND GEIR 0. STORVIK. ” A Simulation Study of Some Contextual Classification Methods For Remotely Sensed Data”, Trans on geosciences and remote sensing,journals of information sciences 1987
  20. Sarika Yadav, Imdad Rizvi, Shailaja Kadam, Luis Iribarne, and James Z. Wang, Urban Tree Canopy Detection Using Object-Based Image Analysis for Very High Resolution Satellite Images : A Literature Review,IEEE Trans on geosciences and remote sensing,journals of information sciences 2015
  21. S. Leguizamn, M. Espnola, R. Ayala, L. Iribarne, and M. Menenti, Characterization of texture in images by using a cellular automata approach,in Proc. 3rd WSKS, vol. 112, CCIS, 2010, no. 2, pp. 522533.
  22. Miao Li, Shuying Zang, Bing Zhang, Shanshan Li and Changshan Wu., A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information, European Journal of Remote Sensing - 2014, 47: 389-411.
  23. F. Carvajal, E. Crisanto, F. J. Aguilar, F. Agera, and M. A. Aguilar, Greenhouses detection using an artificial neural network with a very high resolution satellite image, in Proc. ISPRS Tech. Commission II Symp., 2006, pp. 3742.
  24. R. Ayala, A. Becerra, I. M. Flores, J. F. Bienvenido, and J. R. Daz, Evaluation of greenhouse covered extensions and required resources with satellite images and GIS. Almeras case, in Proc. 2nd Eur. Conf. Eur. Fed. Inf. Technol. Agriculture, Food Environ., 1999, pp. 2730.
  25. Moiss Espnola, Jos A. Piedra-Fernndez, Rosa Ayala, Luis Iribarne, and James Z. Wang,Contextual and Hierarchical Classification of Satellite Images Based on Cellular Automata,IEEE Trans on geosciences and remote sensing, journals of information sciences 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Classification Supervised classifiers Contextual classification Cellular Automata