Cloud Identification Method Using HOS Based ICA For Multispectral NOAA Image

T. Venkata Krishnamoorthy *, G. Umamaheswara Reddy **
* Research Scholar, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India..
** Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India
Periodicity:February - April'2018
DOI : https://doi.org/10.26634/jfet.13.3.14229

Abstract

The Independent Component Analysis (ICA) is showing a vital role in separating the image objects and dimensional reduction. The clouds are very important in the National Oceanic and Atmospheric Administration (NOAA) multispectral image. The removal of total cloud is very difficult, so these clouds are classified using this proposed technique. Using ICA and k-means clustering algorithm, the different types of clouds are classified and clouds are separated from the water and ground levels. These values are verified by temperature values of Ch4 and Ch5 bands, individual bands, Normalized Difference Vegetation Index (NDVI), and albedo reflectance values with Ch1 and Ch2. This algorithm shows optimum results compared with threshold and surface edge detection methods. The performance of this proposed method has been evaluated visually with good efficiency.

Keywords

NOAA, ICA, NDVI, Albedo Reflectance, K-means Clustering.

How to Cite this Article?

Krishnamoorthy, T. V., and Reddy, G. U. (2018). Cloud Identification Method Using HOS Based ICA For Multispectral NOAA Image. i-manager’s Journal on Future Engineering and Technology,13(3), 35-41. https://doi.org/10.26634/jfet.13.3.14229

References

[1]. Allen Jr, R. C., Durkee, P. A., & Wash, C. H. (1990). Snow/cloud discrimination with multispectral satellite measurements. Journal of Applied Meteorology, 29(10), 994-1004.
[2]. Baatz, M., & Schape, A. (2000). Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation. In Strobl J., Blaschke T., & Greisebener G. (Eds.), Angewandte Geographische Informationsverarbeitung XII. Beitrage zum AGITSymposium Salzburg 2000.
[3]. Bajwa, I. S., Naweed, M. S., Asif, M. N., & Hyder, S. I. (2009). Feature based image classification by using principal component analysis. ICGST-GVIP Journal, 9(2), 11-17.
[4]. Benlina, X., Fangfang, L., Xingliang, M., & Huazhong , J. (2008). Study on independent component analysis application in classification and change detection of multispectral images. The International Archives of the Photogrammetr y, Remote Sensing and Spatial Information Sciences, 37(B7), 871-876.
[5]. Gitas, I. Z., Mitri, G. H., & Ventura, G. (2004). Objectbased image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery. Remote Sensing of Environment, 92(3), 409-413.
[6]. Katagiri, S., & Nakajima, T. (2004). Radiative characteristics of cirrus clouds as retrieved from AVHRR. Journal of the Meteorological Society of Japan. Ser. II, 82(1), 81-99.
[7]. Krishnamoorthy, T. V., & Reddy, G. U. (2018). Fusion Enhancement of Multispectral Satellite Image by Using Higher Order Statistics. Asian Journal of Scientific Research, 11(2), 162-168.
[8]. Kubo, M., & Muramoto, K. I. (2007, July). Classification of clouds in the Japan Sea area using NOAA AVHRR satellite images and self-organizing map. In Geoscience a n d R e m o t e S e n s i n g S y m p o s i u m , 2 0 0 7 . I E E E International (pp. 2056-2059). IEEE.
[9]. Liu, Y., Xia, J., Shi, C. X., & Hong, Y. (2009). An improved cloud classification algorithm for China's FY-2C multi-channel images using artificial neural network. Sensors, 9(7), 5558-5579.
[10]. Mao, J., & Jain, A. K. (1992). Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition, 25(2), 173- 188.
[11]. Nordin, A., Hsu, C. C., & Szu, H. H. (2001). Design of FPGA ICA for hyperspectral imaging processing. In Wavelet Applications VIII (Vol. 4391, pp. 444-455). International Society for Optics and Photonics.
[12]. Rodríguez-Yi, J. L., Shimabukuro, Y. E., & Rudorff, B. F. T. (2000). Image segmentation for classification of vegetation using NOAA-AVHRR data. International Journal of Remote Sensing, 21(1), 167-172.
[13]. Sun, J. (2000). Dynamic monitoring and yield estimation of crops by mainly using the remote sensing technique in China. Photogrammetric Engineering and Remote Sensing, 66(5), 645-650.
[14]. Townshend, J. R. G. (1994). Global data sets for land applications from the Advanced Very High Resolution Radiometer: An introduction. International Journal of Remote Sensing, 15(17), 3319-3332.
[15]. Urbanek, B., Groß, S., Schäfler, A., & Wirth, M. (2017). Determining stages of cirrus evolution: A cloud classification scheme. Atmospheric Measurement Techniques, 10(5), 1653-1664.
[16]. Wang, J., & Chang, C. I. (2006). Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 44(6), 1586-1600.
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.