A Comparative Study of Fuzzy Techniques Used for High Resolution Imagery

Authors

  • N THINAHARAN Thanthai Hans Roever College, Bharathidasan University, Perambalur 621212, Tamilnadu, India
  • SRIMATHI P Thanthai Hans Roever College, Bharathidasan University, Perambalur 621212, Tamilnadu, India

Keywords:

Adaptive Fuzzy C-Means, Modified Fuzzy C-Means, Texture Segmentation, region merging

Abstract

Image segmentation is crucial to object-oriented remote sensing imagery analysis. A novel texture segmentation algorithm is proposed for high-resolution remote sensing imagery, in which texture clustering is first carried out as loose constraint for later segmentation. The algorithm is based on region adjacency graph models of region adjacency graph, which can achieve fast node merging, defending on the global optimum. Here the spectral, texture and shape features, is established to measure the similarity between nodes and gives the same semantic descriptions for the texture objects. During the merging process, optimal sequence merging interacts with texture clustering to refine the real edges of a texture region. This algorithm cannot only merge the homogenous texture segments with spectral variability easily but can also detect the real object boundaries well. It found that the execution time of modified Fuzzy clustering techniques decreases the number of clusters increases. But in the other techniques the execution time increases when the numbers of clusters increases and detect the boundaries not well. The Modified Fuzzy Techniques detect the hidden details with more accuracy.

References

U. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, ―Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information,‖ ISPRS J. Photogramm. Remote Sens., vol. 58, no. 3/4, pp. 239–258, Jan. 2004.

T. Blaschke, S. Lang, and G. Hay, Object-Based Image Analysis: Spatial Concepts for KnowledgeDriven Remote Sensing Applications. Berlin,

Germany: Springer-Verlag, 2008.

G. Willhauck, ―Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos,‖ Int. Arch. Photogramm. Remote Sens., vol. XXXIII, pp. 35–42, 2000, Supplement B3.

G. Dong and M. Xie, ―Color clustering and learning for image segmentation based on neural networks,‖ IEEE Trans. Neural Netw., vol. 16, no. 4, pp. 925–936, Jul. 2005.

D. Comaniciu and P.Meer, ―Mean shift: A robust approach toward feature space analysis,‖ IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603–619, May 2002.

C. Rother, V. Kolmogorov, and A. Blake, ―GrabCut: Interactive foreground extraction using iterated graph cuts,‖ in ACM Trans. Graph., vol. 23, no. 3, pp. 309–314, Aug. 2004.

L. Shafarenko, M. Petrou, and J. Kittler, ―Automatic watershed segmentation of randomly textured color images,‖ IEEE Trans. Image Process., vol. 6, no. 11, pp. 1530–1544, Nov. 1997.

Y. Zhao, L. Zhang, P. Li, and B. Huang, ―Classification of high spatial resolution imagery using improved Gaussian Markov random-fieldbased texture features,‖ IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5,pp. 1458–1468, May 2007.

H.-C. Hsin, ―Texture segmentation using modulated wavelet transform,‖ IEEE Trans. Image Process., vol. 9, no. 7, pp. 1299–1302, Jul. 2000.

T. Bianchi, F. Argenti, and L. Alparone, ―Segmentation-based MAP despeckling of SAR Images in the undecimated wavelet domain,‖ IEEE

Trans. Geosci. Remote Sens., vol. 46, no. 9, pp. 2728–2742, Sep. 2008.

T. Randen and J. H. Husoy, ―Texture segmentation using filters with optimized energy separation,‖ IEEE Trans. Image Process., vol. 8, no.

, pp. 571–582, Apr. 1999.

X. Zhang, L. Jiao, and F. Liu, ―Spectral clustering ensemble applied to SAR image segmentation,‖ IEEE Trans. Geosci. Remote Sens., vol. 46, no. 7, pp. 2126–2136, Jul. 2008.

J. Chen, T. Pappas, A. Mojsilovic, and B. Rogowitz, ―Adaptive perceptual spectral–texture image segmentation,‖ IEEE Trans. Image Process., vol. 14, no. 10, pp. 1–13, Oct. 2005.

Y. Deng and B. Manjunath, ―Unsupervised segmentation of spectral–texture regions in images and video,‖ IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 8, pp. 800–810, Aug. 2001.

H. G. Akçay and S. Aksoy, ―Automatic detection of geospatial objects using multiple hierarchical segmentations,‖ IEEE Trans. Geosci.

Remote Sens., vol. 46, no. 7, pp. 2097–2111, Jul. 2008.

K. Haris, S. Efstratiadis, N. Maglaveras, and A. Katsaggelos, ―Hybrid image segmentation using watersheds and fast region merging,‖ IEEE Trans. Image Process., vol. 7, no. 12, pp. 1684–1699, Dec. 1998.

X.Wu, ―Adaptive split-and-merge segmentation based on piecewise leastsquare approximation,‖ IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 8, pp. 808–815, Aug. 1993.

Q. Yu and D. A. Clausi, ―IRGS: Image segmentation using edge penalties and region growing,‖ IEEE Trans. Pattern Anal. Mach. Intell.,

vol. 30, no. 12, pp. 2126–2139, Dec. 2008.

B. Manjunath and W. Y. Ma, ―Texture features for browsing and retrieval of image data,‖ IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 837–842, Aug. 1996.

P. Kruizinga, N. Petkov, and S. E. Grigorescu, ―Comparison of texture features based on Gabor filters,‖ in Proc. 10th Int. Conf. Image Anal. Process., 1999, pp. 142–147.

U. Kaymak and M. Setnes, ―Fuzzy clustering with volume prototypes and adaptive cluster merging,‖ IEEE Trans. Fuzzy Syst., vol. 10, no. 6, pp. 705–712, Dec. 2002.

A. Liew, H. Yan, and N. Law, ―Image segmentation based on adaptive cluster prototype estimation,‖ IEEE Trans. Fuzzy Syst., vol. 13, no. 4,

pp. 444–453, Aug. 2005.

T. Esch, M. Thiel, M. Bock, A. Roth, and S. Dech, ―Improvement of image segmentation accuracy based on multiscale optimization procedure,‖ IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3, pp. 463–467, Jul. 2008.

M. Neubert, H. Herold, and G. Meinel, ―Evaluation of remote sensing image segmentation quality—Further results and concepts,‖ in Proc. 1st Int. Conf. OBIA, 2006, pp. 6–11.

. M.N.Ahmed, S.M.Y amany, N.Mohamed, A.Farag, T.Moriarty, ―A modified fuzzy C-means algorithm for bias Field estimation and segmentation of MRI data‖, IEEE Transaction on Medical Imaging 21(3) (2002)193-199.

. Richard Nock and Frank Nielsen, ―Statistical Region Merging‖ , IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, November 2004

. Nan Li, Hong Huo, and Tao Fang, ―A Novel Texture-Preceded Segmentation Algorithm for HighResolution Imagery‖, IEEE Transactions on Geoscience and Remote Sensing, vol. 48, No. 7, July 2010.

Downloads

Published

2024-02-26

How to Cite

N, T., & P, S. (2024). A Comparative Study of Fuzzy Techniques Used for High Resolution Imagery. COMPUSOFT: An International Journal of Advanced Computer Technology, 4(05), 1766–1772. Retrieved from https://ijact.in/index.php/j/article/view/310

Issue

Section

Review Article

Similar Articles

<< < 1 2 3 4 5 6 7 8 9 > >> 

You may also start an advanced similarity search for this article.