You are here

Home » Content

IMAGE QUALITY PARAMETERS FOR THE ANALYSIS OF SEGMENTATION OF SATELLITE IMAGES IN TWO DIFFERENT COLOR SPACES

Journal Name:

  • Indian Streams Research Journal

Publication Year:

  • 2013

Keywords (Original Language):

Author NameUniversity of Author
Bookmark/Search this post with
Abstract (Original Language): 
Image quality parameters are the figure of merit widely used in the image processing applications to analyze and compare the output image with the input image. These measures are widely used in image compression, segmentation, feature extraction, object detection and tracking, and image based measurements. In this paper, these parameters measure the similarity or dissimilarity between the two images on the basis of comparing the corresponding pixels of the two images and present a numerical value as a result. The segmentation is one of the most challenging and important process in the image analysis. The success of the image analysis is based on the result produced in the segmentation stage. This paper presents a comparative study of the segmentation of high resolution satellite images in RGB and HSV color spaces using modified k-means clustering algorithm. The segmented images are compared with the original input images by using number of bivariate image quality parameters. To test the efficiency and robustness of the proposed method, the experiments are performed on GeoEye-1 satellite images.
FULL TEXT (PDF): 
PDF icon arastrmx_177287_3_pp_1-8.pdf
  • 9
1-8
  • English

REFERENCES

References: 

I. J. Pérez, R. Pazos , L. Cruz, G. Reyes, R.
Basave, and H. Fraire “Improving the Efficiency
and Efficacy of the K-means Clustering
Algorithm Through a New Convergence
Condition”, Gervasi and M. Gavrilova (Eds.):
II. S. J. Redmond, C. Heneghan, “A method for
initialising the K-means clustering algorithm
using kd-trees. Science direct”, Pattern
Recognition Letters 28 (2007) 965–973.
III. Ganesan, P., and V. Rajini. "Segmentation and
edge detection of color images using CIELAB
color space and edge detectors." Emerging
Trends in Robotics and Communication
Technologies (INTERACT), 2010 International
Conference on. IEEE, 2010.
IV. Ganesan, P., and V. Rajini. "A method to
segment color images based on modified fuzzypossibilistic-
c-means clustering algorithm."
Recent Advances in Space Technology Services
and Climate Change (RSTSCC), 2010. IEEE,
2010.
V. Zhengjian Ding, Jin Sun, and Yang Zang, “FCM
Image Segmentation algorithm based on color
space and spatial information”, International
journal on computer and communication,Vol
2,No 1,2013
VI. X.Li, X.Lu, J.Tian,” Application of fuzzy cmeans
clustering in data analysis of
metabolomics”, Analytical chemistry, Vol 80,
no.11, pp. 4468-4475, 2009.
VII. http://learn.colorotate.org/color-models.html
VIII. www.equqsys.de
IX. http://www.mathworks.com/
X. R. C. Gonzalez, 2006. Digital Image Processing,
Prentice Hall of India, Second Edition.
XI. Sahaphong,S, “Unsupervised Image
segmentation using automated Fuzzy c-Means”,
7th IEEE Conference on computer and IT,
Bangkok, 690-694,2007
XII. A.M. Eskicioglu and P.S. Fisher, “A survey of
quality measures for gray scale image
compression”, Proceedings of Space and Earth
Science Data Compression Workshop (Nasa
Conference Publication 3191), Utah, Apr 02,
1993, pp.49-61.
XIII. A.M. Eskicioglu and P.S. Fisher, “Image Quality
Measures and Their Performance”, IEEE
Transactions on Communications, Volume 43,
No. 12, Dec 1995 pp. 2959-2965.
XIV. Ismail Avcıbas¸ Bulent Sankur and Khalid
Sayood (2002) ‘Statistical evaluation of image
quality measures’, Journal of Electronic Imaging
Vol. 11, No. 2, pp. 206-223.
XV. Hamid Rahim Sheikh, Muhammad Farooq Sabir
and Alan C. Bovik (2006) ‘A statistical
evaluation of recent full reference image quality
assessment algorithms’, IEEE Trans Image
Processing, Vol. 15, No. 11, pp. 3440-3451
XVI. NoorA.Ibraheem and MokhtarM.Hasan,
“Understanding Color Models: A Review”,
ARPN Journal of Science and
technology,Vol.2,No.3,Apr 2012,pp.265-275.
XVII. Yang, ” Image segmentation by Fuzzy C Means
Clustering Algorithm with a novel penalty term”,
Computing and Informatics, Vol. 26,17-31,2007
XVIII. T. Kanungo, D. Mount, N. Netanyahu, C. Piatko,
R. Silverman, and A. Y. Wu “An efficient kmeans
clustering algorithm: analysis and
implementation,” IEEE Trans. on Pattern
Analysis and Machine Intelligence, vol. 24, No.
7, 2002.
XIX. www.satelliteimagingcorporation.com
XX. www.geoeye.com

Thank you for copying data from http://www.arastirmax.com