ICTACT Journal on Image and Video Processing

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Curvelet Based Satellite Image Natural Resource Classification System Using EIM


Affiliations
1 Department of Computer Science, Jawaharlal Nehru Technological University, Anantapur, India
2 Department of Computer Science, Vasavi College of Engineering, India
3 Department of Computer Science, JNTUA College of Engineering, India
     

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Remote sensing is one of the hottest topics of research, which intends to study or analyze a particular object in the topographic map. The monitoring and management is possible when it is possible to differentiate the objects in the satellite image. However, satellite image classification is not easy, as it consists of numerous minute details. In addition to this, the accuracy and faster execution of the classification system are significant factors. This article presents a satellite image classification system that is capable of differentiating between soil, vegetation and water bodies. To achieve the goal, we categorize the entire system into three major phases; they are satellite image pre-processing, feature extraction and classification. The initial phase attempts to denoise the satellite image by the adaptive median filter and the contrast enhancement is done by Contrast Limited Adaptive Histogram Equalization (CLAHE). As the satellite image possess many important features, this work extracts curvelet moments by applying curvelet transform. The feature vector is formed out of these curvelet moments and the ELM classifier is used to train these features. The performance of the proposed approach is observed to be satisfactory in terms of sensitivity, specificity, and accuracy.

Keywords

Remote Sensing, Satellite Image Classification, Feature Extraction.
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  • P. Mather and B. Tso, “Classification Methods for Remotely Sensed Data”, 2nd Edition, CRC Press, 2009.

  • Sunitha Abburu and Suresh Babu Golla, “Satellite Image Classification Methods and Techniques: A Review”, International Journal of Computer Applications, Vol. 119, No. 8, pp. 20-25, 2015

  • Pooja Kamavisdar, Sonam Saluja and Sonu Agrawal, “A Survey on Image Classification Approaches and Techniques”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, No. 1, pp. 1005-1009, 2013.

  • Shabnam Jabari and Yun Zhang, “Very High Resolution Satellite Image Classification using Fuzzy Rule-Based Systems”, Algorithms, Vol. 6, No. 4, pp. 762-781, 2013.

  • M. Chandrakala and R. Amsaveni, “Classification of Remote Sensing Image Areas using Surf Features and Latent Dirichlet Allocation”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, No. 9, pp. 178-182, 2013.

  • S. Muhammad, G. Aziz, N. Aneela and S. Muhammad, “Classification by Object Recognition in Satellite Images by using Data Mining”, Proceedings of International Conference on World Congress on Engineering, pp. 1-6, 2012.

  • A. Selim, “Spatial Techniques for Image Classification”, CRC Press, 2006.

  • Satellite Imaging Corporation, Available at: www.satimagingcorp.com

  • E. Candes and D. Donoho, “Curvelets: A Surprisingly Effective Non-Adaptive Representation for Objects with Edges”, Proceedings of IEEE International Conference on Image Processing, pp. 105-120, 2000.

  • L. Li, X. Zhanga, H. Zhanga, X. Hea and M. Xua, “Feature Extraction of Non-Stochastic Surfaces using Curvelets”, Precision Engineering, Vol. 39, No. 2, pp. 212-219, 2015.

  • L. Dettori and L. Semler, “A Comparison of Wavelet, Ridgelet, and Curvelet-based Texture Classification Algorithms in Computed Tomography”, Computers in Biology and Medicine, Vol. 37, No. 4, pp. 486-498, 2007.

  • F. Murtagh and J. Starck, “Wavelet and Curvelet Moments for Image Classification: Application to Aggregate Mixture Grading”, Pattern Recognition Letters, Vol. 29, No. 10, pp. 1557-1564, 2008.

  • C. Dalfo, M. Fiol and E. Garriga, “Moments in Graphs”, Discrete Applied Mathematics, Vol. 161, No. 6, pp. 768-777, 2013.

  • Guang-Bin Huang, Hongming Zhou, Xiaojian Ding and Rui Zhang, “Extreme Learning Machine for Regression and Multiclass Classification”, IEEE Transactions on Systems, Man and Cybernetics-Part B, Vol. 42, No. 2, pp. 513-529, 2012.


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  • Curvelet Based Satellite Image Natural Resource Classification System Using EIM

Abstract Views: 263  |  PDF Views: 4

Authors

Anita Dixit
Department of Computer Science, Jawaharlal Nehru Technological University, Anantapur, India
Nagaratna P. Hegde
Department of Computer Science, Vasavi College of Engineering, India
B. Eswara Reddy
Department of Computer Science, JNTUA College of Engineering, India

Abstract


Remote sensing is one of the hottest topics of research, which intends to study or analyze a particular object in the topographic map. The monitoring and management is possible when it is possible to differentiate the objects in the satellite image. However, satellite image classification is not easy, as it consists of numerous minute details. In addition to this, the accuracy and faster execution of the classification system are significant factors. This article presents a satellite image classification system that is capable of differentiating between soil, vegetation and water bodies. To achieve the goal, we categorize the entire system into three major phases; they are satellite image pre-processing, feature extraction and classification. The initial phase attempts to denoise the satellite image by the adaptive median filter and the contrast enhancement is done by Contrast Limited Adaptive Histogram Equalization (CLAHE). As the satellite image possess many important features, this work extracts curvelet moments by applying curvelet transform. The feature vector is formed out of these curvelet moments and the ELM classifier is used to train these features. The performance of the proposed approach is observed to be satisfactory in terms of sensitivity, specificity, and accuracy.

Keywords


Remote Sensing, Satellite Image Classification, Feature Extraction.

References