Segmentation Images Using Improved Genetic Algorithm

Authors

  • Kailasam Leelavathi Department of Computer Science, Vikrama Simhapuri University, Nellore, Andhra Pradesh, India
  • T. Sudha Department of Computer Science, Sri Padmavati Mahila Visva Vidhyalayam, Tirupati, Andhra Pradesh, India

DOI:

https://doi.org/10.51983/ajcst-2019.8.S3.2081

Keywords:

Segmentation Images, Hyper-spectral Imaging, Aerial Photography, Satellite Image Segmentation

Abstract

With the expanding openness to new advancements, the principle issues in locale acknowledgment of remote detecting pictures are: (1) arrangement techniques are reliant on the division quality; and (2) the choice of delegate tests for preparing. The significant test is that the examples shown by the client are not in every case enough to characterize the best division scale. Besides, the sign of tests can be expensive, since it regularly requires visiting considered places in loco. The choice of delegate tests, then again, was bolstered in this work by the improvement of another intelligent characterization approach based on dynamic learning. Critical commitments were likewise acquired concerning the depiction of areas in remote detecting pictures by methods for: an assessment investigation of 19 descriptors; and two new methodologies for accelerating highlight extraction from a progressive system of sectioned districts.

References

K. Perumal and R. Bhaskaran, "SVM-Based Effective Land Use Classification System For Multispectral Remote Sensing Images," International Journal of Computer Science and Information Security (IJCSIS), vol. 6, no. 2, pp. 95-107, 2009.

J. Knorn, A. Rabe, V. C. Radeloff, T. Kuemmerle, J. Kozak, and P. Hostert, "Land cover mapping of large areas using chain classification of neighboring Landsat satellite images," Remote Sensing of Environment, vol. 118, pp. 957-964, 2009.

X. Zou and D. Li, "Application of Image Texture Analysis to Improve Land Cover Classification," WSEAS Transactions on Computers, vol. 8, no. 3, pp. 449-458, March 2009.

R. A. El-Khoribi, "Support Vector Machine Training of HMT Models for Multispectral Image Classification," IJCSNS International Journal of Computer Science and Network Security, vol. 8, no. 9, pp. 224-228, September 2008.

B. Sowmya and B. Sheelarani, "Land cover classification using reformed fuzzy C-means," Sadhana, vol. 36, no. 2, pp. 153–165, 2011.

V. K. Panchal, P. Singh, N. Kaur, and H. Kundra, "Biogeography based Satellite Image Classification," International Journal of Computer Science and Information Security (IJCSIS), vol. 6, no. 2, pp. 269-274, November 2009.

B. Huang, C. Xie, R. Tay, and B. Wu, "Land-use-change modeling using unbalanced support-vector machines," Environment and Planning B: Planning and Design, vol. 36, no. 3, pp. 398-416, 2009.

J. A. Shine and D. B. Carr, "A Comparison of Classification Methods for Large Imagery Data Sets," JSM 2002 Statistics in an ERA of Technological Change-Statistical Computing Section, New York City, pp. 3205-3207, 11-15 Aug. 2002.

D. Lu and Q. Weng, "A survey of image classification methods and techniques for improving classification performance," International Journal of Remote Sensing, vol. 28, no. 5, pp. 823-870, January 2007.

M. Govender, K. Chetty, V. Naiken, and H. Bulcock, "A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation," Water SA, vol. 34, no. 2, April 2008.

M. F. Jasinski, "Estimation of subpixel vegetation density of natural regions using satellite multispectral imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 34, pp. 804–813, 1996.

C. Palaniswami, A. K. Upadhyay, and H. P. Maheswarappa, "Spectral mixture analysis for sub-pixel classification of coconut," Current Science, vol. 91, no. 12, pp. 1706 -1711, 25 December 2006.

S. Chen, S. R. Gunn, and C. J. Harris, "The Relevance Vector Machine Technique for Channel Equalization Application," IEEE Transactions on Neural Networks, vol. 12, no. 6, November 2001.

P.-K. Wong and Q. Xu, "Rate-Dependent Hysteresis Modeling and Control of a Piezostage Using Online Support Vector Machine and Relevance Vector Machine," IEEE Transactions on Industrial Electronics, vol. 59, no. 4, April 2012.

B. Gholami, "Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging," IEEE Transactions on Biomedical Engineering, vol. 57, no. 6, June 2010.

M. Pal and G. M. Foody, "Evaluation of SVM, RVM and SMLR for Accurate Image Classification with Limited Ground Data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 5, October 2012.

A. Ch. Braun, U. Weidner, and S. Hinz, "Classification in High-Dimensional Feature Spaces—Assessment Using SVM, IVM and RVM with Focus on Simulated EnMAP Data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 2, April 2012.

F. A. Mianji, Y. Zhang, and A. Babakhani, "Nonlinear discriminant analysis and RVM for efficient classification of small land-cover patches," IEEE Conference Publications Communications and Signal Processing (ICCSP), 2011.

H. Uehara, H. Watanabe, S. Katagiri, and M. Ohsaki, "Comparison between Minimum Classification Error training and Relevance Vector Machine," IEEE Conference Publications TENCON 2012, 2012.

A. Babaeean, A. B. Tashk, M. Bandarabadi, S. Rastegar, "Target Tracking Using Wavelet Features and RVM Classifier," IEEE Conference Publications Natural Computation, 2008. ICNC ’08.

G. Camps-Valls and L. Bruzzone, "Kernel-based methods for Hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1352–1362, Jun. 2005.

M. E. Tipping, "The relevance vector machine," in Advances in Neural Information Processing Systems, vol. 12, S. A. Solla, T. K. Leen, and K. R. Müller, Eds. Cambridge, MA: MIT Press, 2000.

M.-H. Tseng, S.-J. Chen, G.-H. Hwang, and M.-Y. Shen, "A genetic algorithm rule-based approach for land-cover classification," Journal of Photogrammetry and Remote Sensing, vol. 63, no. 2, pp. 202-212, 2008.

P. O. Gislason, J. A. Benediktsson, and J. R. Sveinsson, "Random Forests for land cover classification," Pattern Recognition Letters, vol. 27, no. 4, pp. 294-300, 2006.

K. Leelavathi and T. Sudha, "Remote image classification using improved decision tree and neural networks," IJECIERD, vol. 5, April 2015.

K. Leelavathi and T. Sudha, "Improving accuracy in Spatial images classification," IJCSEITR, vol. 5, pp. 43-48, June 2015.

Downloads

Published

15-05-2019

How to Cite

Leelavathi, K., & Sudha, T. (2019). Segmentation Images Using Improved Genetic Algorithm. Asian Journal of Computer Science and Technology, 8(S3), 81–84. https://doi.org/10.51983/ajcst-2019.8.S3.2081

Most read articles by the same author(s)