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A Survey of Various Algorithms Used on Multispectral Satellite Image Classification of Alwar Image Dataset

Affiliations

  • Department of Computer Science Pondicherry University, Pondicherry - 605014, Tamil Nadu, India

Abstract


Objectives: The aim of the survey was to study and compare the efficacy of using bio inspired algorithms for the purpose of classification of a satellite dataset. The final objectives involved identification of a suitable technique to most effectively classify a satellite image using various strategies. Methods/Statistical Analysis: Bio Inspired Algorithms (BIA) are increasingly being used in classification tasks as they are extremely efficient and can generate solutions for complex associations from simple initial settings. The paper presents a survey and review of the use of the BIAs in the domain of satellite image classification to segregate similar pixels with common values for different bands of data and classifying them into various classes. This study concentrates on a multispectral satellite data of size 472 X 576 (257712 pixels) of the Alwar region, Rajasthan having 7 bands of attribute information. The data is classified into classes of major land-use types. Findings: The study has taken into account various strategies used by different researchers to produce the best classification accuracies for classifying a satellite image dataset. This includes the process of feature selection and feature reduction wherein a combination of classifiers are used as ensemblers or in a hybrid model to achieve better classification. The different classification approaches like per pixel, sub pixel, object based and knowledge based classification has been studied and categorized based on their usage. The parameters used to determine the efficacy comparisons for these classifications are namely kappa coefficient, producers, users and overall accuracy. The various classification strategies have been compared based on their kappa coefficient performance. Cuckoo search algorithm and artificial bee colony, two of the recent bio inspired algorithms has shown an impressive classification accuracy and comparable performances are given by the variants of ant colony optimization algorithm in different hybrid models with PSO and BBO. The performances of per pixel and object based classification methodology are similar and the higher accuracies are determined by the use of better classification algorithm. Application/Improvements: In addition certain application usage of the bio inspired classification algorithm in the domain of remote sensing has also been studied. To substantiate the efficient performances of these bio inspired algorithms in the satellite image classification; the effect of these on different sets of satellite images has also been studied and found to be good.

Keywords

Ant Colony Optimization (ACO), Biogeography Based Optimization (BBO), Bio Inspired Algorithms, Classification Accuracy, Particle Swarm Optimization (PSO), Satellite Image Classification.

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References


  • Roiger R.J, Geatz MW. Data Mining: A Tutorial-Based Primer. Boston: Addison Wesley; 2003.
  • Song X, Cherian G, Fan G. A ν-insensitive SVM approach for compliance monitoring of the conservation reserve program. IEEE Geoscience and Remote Sensing Letters. 2005; 2 (2): 99–103.
  • Sanderson R. Introduction to Remote Sensing. New Mexico State University;p. 25-6.
  • Binitha S, SivaSathya S. A Survey of Bio inspired Optimization Algorithms. International Journal of Soft Computing and Engineering (IJSCE). 2012 May; 2(2):137-51.
  • Lu D, Weng D. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing. March 2007; 28(5): 823–70.
  • Panchal VK, Kundra H, Kaur J. Comparative Study of Particle Swarm Optimization based Unsupervised Clustering Techniques. International Journal of Computer Science and Network Security. 2009; 9(10):132-40.
  • Thomas N, Hendrix C, Congalton RG. A comparison of urban mapping methods using high-resolution digital imagery. Photogrammetric Engineering and Remote Sensing. 2003; 69(9):963–72.
  • Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS- ready information. ISPRS Journal of Photogrammetry & Remote Sensing. 2004; 58(3-4):239–58.
  • Gitas IZ, Mitri GH, Ventura G. Object-based image classification for burned area mapping of Creus Cape Spain, using NOAA-AVHRR imagery. Remote Sensing of Environment. 2004;, 92(3): 409–13.
  • Walter V, Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry & Remote Sensing. 2004; 58(3-4): 225–38.
  • Janssen LF, Molenaar M. Terrain objects, their dynamics and their monitoring by integration of GIS and remote sensing. IEEE Transactions on Geoscience and Remote Sensing. 1995; 33(3):749–58.
  • Defries RS, Chan JC. Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sensing of Environment. 2000; 74(3): 503–15.
  • Goel S, Sharma A, Bedi P, Panchal V K. Decision Gap within Swarm in Natural Terrain Feature Identification. International Journal of Computer Science and Security. 2011; 1(3):1-15.
  • Kennedy J, Eberhart R. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks. IV. 1995;p. 1942-48.
  • Dorigo M, Maniezzo V, Colorni A. Ant System: Optimization of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics – Part B. 1996; 26(1): 29-41.
  • Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization. 2007; 39(3): 459-71.
  • Dasgupta D. Artificial Immune System and Their Applications. Berlin: Springer;1999.
  • Simon D. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation. 2008; 12(6):702-13.
  • Xin-She Yang, Suash Deb. Cuckoo search via Levy flights, In Proceedings of World Congress on Nature & Biologically Inspired Computing, (NaBIC), IEEE Publications USA. 2009;p. 210-14.
  • Payne R B, Sorenson M D, Klitz K. The Cuckoos, Oxford University Press, 2005.
  • Bharadwaj A, Gupta D, Panchal V K. Applying Nature Inspired Metaheuristic Technique to Capture the Terrain Features, Proceedings of the 2012 International Conference on Artificial Intelligence. 2012;991-96.
  • Holland JH. Genetic algorithm and the optimal allocation of trials. SIAM Journal of Computation. 1973; 2 (2): 88-105.
  • Omkar SN, Manoj MK, Dheevatsa M, Dipti M. Urban Satellite Image Classification using Biologically Inspired Techniques. IEEE International Symposium on Industrial of the matrix Electronics. 2007; 1767 – 772.
  • Laha A. On some new methodologies for pattern recognition aided by self-organizing maps. 2005.
  • Goel L, Gupta D, Panchal VK. Hybrid bio-inspired techniques for land cover feature extraction : A remote sensing perspective. Applied Soft Computing Journal. 2012; 12(2):832–49.
  • Johal NK, Singh S, Kundra H. A hybrid FPAB / BBO Algorithm for Satellite Image Classification, International Journal of Computer Applications. 2010; 6(5):31–6.
  • Arora P, Kundra H, Panchal VK. Fusion of Biogeography based optimization and artificial bee colony for identification of Natural Terrain Features. International Journal of Advanced Computer Science and Applications. 2012; 3(10):107–11.
  • Banerjee S, Bharadwaj A, Gupta D, Panchal VK. Remote sensing image classification using Artificial Bee Colony algorithm. International Journal of Computer Science and Informatics. 2012; 2(3): 67–72.
  • Kundra H, Puja, Panchal VK. Cross-Country Path Finding using Hybrid approach of BBO and ACO. International Journal of Computer Applications. 2010; 7(6):20–4.
  • Sood M, Kaur M. Shortest Path Finding in Country using Hybrid approach of BBO and BCO. International Journal of Computer Applications. 2012; 40(6): 9–13.
  • Xiaoping L, Xia L, Xiaojuan P, Haibo L, Jinqiang H E. Swarm intelligence for classification of remote sensing data, Science in China Series D: Earth Sciences Springer. 2008; 51(1): 79–87.
  • Tseng M, Chen S, Hwang G, Shen M. A genetic algorithm rule- based approach for land-cover classification., Journal of Photogrammetry and Remote Sensing. 2008; 63(2): 202-12.
  • Zhong Y, Zhang L, Gong J, Li P. A Supervised Artificial Immune Classifier. IEEE Transactions on Geoscience and Remote Sensing.2007; 45(12): 3957–66.

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