Detection of Clouds Using SVD and Spectral Properties for NOAA AVHRR Imagery

B.Ravi Kumar*, B. Anuradha**
* Research Scholar, Department of Electronics and Communication Engineering, S. V. University, Tirupati, India.
** Professor and Head, Department of Electronics and Communication Engineering, S. V. University, Tirupati, India.
Periodicity:April - June'2017
DOI : https://doi.org/10.26634/jip.4.2.13748

Abstract

Analysis of clouds and their physical properties, such as liquid water content, ice water content, and Reflectivity plays a crucial role in examining the precipitation rate. Till now much work has been done on the NOAA data to examine the clouds and their relation to the rainfall rate. In present work, clouds are detected and classified based on the NOAA-18 AVHRR (Advance Very High Resolution Radiometer) satellite imagery using the SVD (Singular Value Decomposition) property. The eigen values in the SVD help to distinguish between land, snow, and ocean based on the spectral features of the NOAA band 1, 2, and 4 images. The proposed method found the detected clouds with accuracy of 60% using statistical measures. The RGB satellite images are extracted from the NOAA-18 data using ERDAS imagine software which are useful for further processing using MATLAB. All the data used in this work are acquired from NOAA-18 AVHRR satellite imagery installed at S.V. University College of Engineering.

Keywords

Clouds, NOAA, Satellite Imagery, Singular Value Decomposition.

How to Cite this Article?

Kumar,.B.R. and Anuradha.B (2017). Detection of Clouds Using SVD and Spectral Properties for NOAA AVHRR Imagery. i-manager’s Journal on Image Processing, 4(2), 10-15. https://doi.org/10.26634/jip.4.2.13748

References

[1]. Baum, B. A., Tovinkere, V., Titlow, J., & Welch, R. M. (1997). Automated cloud classification of global AVHRR data using a fuzzy logic approach. Journal of Applied Meteorology, 36(11), 1519-1540.
[2]. Chen, J. (2000). Image compression with SVD. ECS 289K Scientific Computation, 13.
[3]. Conover, J. H. (1963). Cloud interpretation from satellite altitudes (No. AFCRL-62-680-Suppl-1). Air Force Cambridge Research Labs Lg Hanscom Field Mass.
[4]. Gonzalez, R. C., & Woods, R. E. (2014). Digital Image Processing, Pearson Education, Inc.,Third Edition.
[5]. Henken, C. C., Schmeits, M., Wolters, E., & Roebeling, R. (2009). Detection of Cb and TCu clouds using MSGSEVIRI cloud physical properties and weather radar observations (Doctoral Dissertation, KNMI WR).
[6]. Haralick, R. M., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610-621.
[7]. Howarth, P., & Rüger, S. M. (2004, July). Evaluation of texture features for content-based image retrieval. In CIVR (Vol. 3115, pp. 326-334).
[8]. Kaur, R., & Ganju, A. (2008). Cloud classification in NOAA AVHRR imageries using spectral and textural features. Journal of the Indian Society of Remote Sensing, 36(2), 167-174.
[9]. Kuo, K. S., Welch, R. M., & Sengupta, S. K. (1988). Structural and textural characteristics of cirrus clouds observed using high spatial resolution LANDSAT imagery. Journal of Applied Meteorology, 27(11), 1242-1260.
[10]. Miller, S. W., & Emery, W. J. (1997). An automated neural network cloud classifier for use over land and ocean surfaces. Journal of Applied Meteorology, 36(10), 1346-1362.
[11]. Pankiewicz, G. S. (1995). Pattern recognition techniques for the identification of cloud and cloud systems. Meteorological Applications, 2(3), 257-271.
[12]. Tag, P. M., Bankert, R. L., & Brody, L. R. (2000). An AVHRR multiple cloud-type classification package. Journal of Applied Meteorology, 39(2), 125-134.
[13]. Welch, R. M., Sengupta, S. K., & Chen, D. W. (1988). Cloud field classification based upon high spatial resolution textural features: 1. Gray level co?occurrence matrix approach. Journal of Geophysical Research: Atmospheres, 93(D10), 12663-12681.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.