Automatic classification for NOAA- AVHRR Data using k-means Algorithm

Authors

  • A. Jyothirmai  Department of ECE, S.V.University College of Engineering, Tirupathi, Andhra Pradesh, India
  • Dr. S. Narayana Reddy  Professor of ECE, S.V.University College of Engineering, Tirupati, Andhra Pradesh, India
  • Dr. P. Jagadamba  Assistant Professor of ECE Ece, Skit, Sri Kalahasthi, Tirupathi, Andhra Pradesh, India

Keywords:

Classification,K-Means, Accuracy Assignment

Abstract

This study proposes associate an automatic classification algorithm rule for NOAA(National Oceanic and Atmospheric Administration)-AVHRR (Advanced Very High-Resolution Radiometer) data It is well known that land cover conditions in the NOAA AVHRR data are classified into three different classes: ocean, land, and cloud. The algorithm consists of two major approaches: region of interest and classification algorithm. the region of interest extracted from the properties of the multispectral bands. k-means algorithm used detect the three classes. classification of image isn’t complete until an accuracy assessment has been conducted for the classified image. An accuracy assessment compares the information of two sources, i.e. pixels of the classified thematic map with reference image.

References

  1. Cao, C. et al. 2008. Assessing the consistency of AVHRR and MODIS L1B reflectance for generating Fundamental Climate Data Records. Journal of Geophysical Research. Vol. 113. D09114. doi: 10.1029/2007JD009363.
  2. Halthore, R. et al. 2008. Role of Aerosol Absorption in Satellite Sensor Calibration. IEEE Geoscience and Remote Sensing Letters. Vol. 5. pp. 157-161.
  3. Heidinger, A. K. et al. 2002. Using Moderate Resolution Imaging Spectrometer (MODIS) to calibrate Advanced Very High-Resolution Radiometer reflectance channels. Journal of Geophysical Research. Vol. 107. doi: 10.1029/2001JD002035.
  4. J. B. MacQueen (1967): "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-the Berkeley Symposium on Mathematical Statistics and Probability", Berkeley, University of California Press, 1:281-297
  5. J. C. D. M. Esquerdo, J. F. G. Antunes, D. G. Baldwin, W. J. Emery, and J. ZulloJr, "An automatic system for avhrr land surface product generation," International Journal of Remote Sensing, vol. 27, pp. 3925-3942, 2006.
  6. D. J. Berndt and J. Clifford, "Using dynamic time warping to find patterns in time series," in Proceedings of the Knowledge Discovery in Databases - KDD Workshop (KDD’1994), Seattle, Washington, USA, 1994, pp. 359- 370, ACM Press..
  7. Kidwell, Katherine B., comp. and ed., 1995, NOAA Polar Orbiter Data (TIROS-N, NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11, NOAA-12, and NOAA-14) Users Guide : Washington, D.C., NOAA/NESDIS.
  8. Harris, J. W. and Stocker, H. "Maximum Likelihood Method." §21.10.4 in Handbook of Mathematics and Computational Science.New York: Springer-Verlag, p. 824, 1998.
  9. Hoel, P. G. Introduction to Mathematical Statistics, 3rd ed. New York: Wiley, p. 57, 1962.
  10. Press, W. H.; Flannery, B. P.; Teukolsky, S. A.; and Vetterling, W. T. "Least Squares as a Maximum Likelihood Estimator." §15.1 in Numerical Recipes in FORTRAN: The Art of Scientific Computing, 2nd ed. Cambridge,: University Press, pp. 651-655, 1992. England

International Journal of Scientific Research in Science and Technology

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Published

2018-04-30

Issue

Volume 4, Issue 5, March-April-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
A. Jyothirmai, Dr. S. Narayana Reddy, Dr. P. Jagadamba "Automatic classification for NOAA- AVHRR Data using k-means Algorithm " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 4, Issue 5, pp.755-762, March-April-2018.