The week's pick
Random Articles
Reseach Article
Performance Analysis of K-Means Clustering For Remotely Sensed Images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 84 - Number 12 |
| Year of Publication: 2013 |
| Authors: K. Venkateswaran, N. Kasthuri, K. Balakrishnan, K. Prakash |
10.5120/14628-2981
|
K. Venkateswaran, N. Kasthuri, K. Balakrishnan, K. Prakash . Performance Analysis of K-Means Clustering For Remotely Sensed Images. International Journal of Computer Applications. 84, 12 ( December 2013), 23-27. DOI=10.5120/14628-2981
Abstract
Remote sensing plays a vital role in overseeing the transformations on the earth surface. Unsupervised clustering has a indispensable role in an immense range of applications like remote sensing, motion detection, environmental monitoring, medical diagnosis, damage assessment, agricultural surveys, surveillance etc In this paper, a novel method for unsupervised classification in multitemporal optical image based on DWT Feature Extraction and K-means clustering is proposed. After preprocessing the optical image is feature extracted using the discrete wavelet transform. On the feature extracted image feature reduction is performed using energy based selection. Finally different K means clustering is performed and analyzed using Matlab and ground truth data for improving classification accuracy.
References
- L. Bruzzone and D. F. Prieto. 2000. "Automatic analysis of the difference image for unsupervised change detection," IEEETrans. Geosci. Remote. Sens, vol. 38, no. 3, pp. 1171-1182.
- Tapas kanungo and Nathan. S An Efficient K Means clustering Algorithm-Analysis and Implementation" IEEE Transactions on Pattern Analysis and Machine intelligence"
- L. Bruzzone and D. F. Prieto. 1996. "An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images,"IEEE Trans. Image Processing. , vol. 11, no. 4, pp. 66-77.
- C. Dumontier, F. Luthon, and J. -P. Charras. 1999. "Real-time DSP implementation for MRF-based video motion detection," IEEETrans. Image Process. , vol. 8, no. 10, pp. 1341–1347.
- R. Collins, A. Lipton, and T. Kanade. 2000 "Introduction to special section on video surveillance," IEEE Trans. Pattern Anal. Mach. Intell. , vol22, no. 8, pp. 745-746.
- K. Grover, S. Quegan and C. da Costa Freitas, 1999. "Quantitative estimation of tropical forest cover by SAR," IEEE Trans. Geosci. Remote Sens. , vol. 37, no. 1, pp. 479–490.
- L. Bruzzone and S. B. Serpico. 1997. "An iterative technique for the detection of land-cover transitions in multitemporal remote- sensing images," IEEE Trans. Geosci. Remote Sens. , vol. 35, pp. 858-867.
- P. S Chavez,Jr and D. J MacKinnon. 1994 "Automatic detection of vegetation changes in the south western united states using remotely sensed images" Photogramm. Eng. Remote Sensing,vol. 60,no. 5
- K. R. Merril and L. Jiajun. 1998 "A comparison of four algorithms for change detection in an urban environment," Remote Sens. Environ. , vol. 63, no. 2, pp. 95–100.
- T. Celik. 2010 "A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images," Signal Process. , vol. 90, no. 5, pp. 1471–1485.
- Y. Bazi, L. Bruzzone, and F. Melgani. 2005. "An unsupervised approach based on the generalized Gaussian model to automatic change detection inmultitemporal SAR images," IEEE Trans. Geosci. Remote Sens. , vol. 43, no. 4, pp. 874–887.
- M. Sezgin and B. Sankur. 2004. "A survey over image thresholding techniques and quantitative performance evaluation," J. Electron. Image. vol. 13, no. 1, pp. 146–165.
- Vrabel, J. : "Multispectral imagery band sharpening study", Photogrammetric Engineering and Remote Sensing, Vol. 62. (1969) 1075-1083.
- R. A Alaguraja, V. Anand and ,S. Maidani. 2009" Wavelet frame based feature extraction technique for improving classification accuracy". J Indian society of remote sensing"pp-423-471.
- R. A Alaguraja,V. Anand,S. Maidani. 2013 "Wavelet based post classification change detection technique for urban growth monitoring". J Indian society of remote sensing" Volume 41, issue 1, pp 35-43
- Grigorious . F. Tzortz Aristisdis C. Likas. 2009 "The global kernel K-Means Algorithm for clustering in feature Space",IEEE Transactions on Neural Networks" vol 20.
- Volpi. M, Tuia. D, Campo-Valls. G and Kanevski. M. 2012 "Unsupervised Change Detection with kernels", IEEE Trans. Geosci. Remote Sens. ,vol. 9, no. 6, pp. 1026–1030.
Index Terms
Keywords