Implementation of GLCM/GLDV-based Texture Algorithm and Its Application to High Resolution Imagery Analysis

GLCM/GLDV 기반 Texture 알고리즘 구현과 고 해상도 영상분석 적용

  • Lee Kiwon (Dept. of Information System Engineering, Hansung University) ;
  • Jeon So-Hee (Dept. of Geoscience Education, Seoul National University) ;
  • Kwon Byung-Doo (Dept. of Geoscience Education, Seoul National University)
  • 이기원 (한성대학교 정보시스템공학과) ;
  • 전소희 (서울대학교 지구과학교육과) ;
  • 권병두 (서울대학교 지구과학교육과)
  • Published : 2005.04.01


Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of the useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program based on GLCM algorithm is newly implemented. As well, texture imaging modules for GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV Texture imaging parameters, it composed of six types of second order texture functions such as Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality in GLCM/GLDV, two direction modes such as Omni-mode and Circular mode newly implemented in this program are provided with basic eight-direction mode. Omni-mode is to compute all direction to avoid directionality complexity in the practical level, and circular direction is to compute texture parameters by circular direction surrounding a target pixel in a kernel. At the second phase of this study, some case studies with artificial image and actual satellite imagery are carried out to analyze texture images in different parameters and modes by correlation matrix analysis. It is concluded that selection of texture parameters and modes is the critical issues in an application based on texture image fusion.


  1. Al-Janobi, A., 2001. Performing evaluation of cross- diagonal Texture matrix method of Texture analysis, Pattern Recognition, 34: 171-180
  2. Bharati, M. H, J. J. Liu, and J. F. MacGregor, 2004. Image Texture analysis: methods and comparisons, Chemometrics and Intelligent Systems, 72: 57-71
  3. Clausi, D. A. and Zhao, Y., 2003. Grey level cooccurrence integrated algorithm (GLCIA): a superior computational method to rapidly determine co-occurrence probability Texture features, Computers & Geosciences, 29: 837-850
  4. Cooper, G. R. J., 2004. The Texture analysis of gravity data using co-occurrence matrices, Computer & Geosciences, 30: 107-115
  5. Demin, X., 2002. Remote Sense and GIS-based Evacuation Analysis, ORNL presentation material, Presentation at the NCRST Interim Conference
  6. Dulyakarn, P., Y. Rangsanseri, and P. Thitimajshima, 2000. Comparison of two features for multispectral imagery analysis, Proceeding of Asian Conference of Remote Sensing
  7. Franklin, S. E., M. A. Wulder, and G. R. Gerylo, 2001. Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia, Int. Jour. of Remote Sensing, 22: 2676-2632
  8. Hall-Beyer, M., 2004. GLCM Texture: A Tutorial v.2.7.1, on-line document, http://www. rial.html
  9. Haralick, R. M., K. Shanmugam, and I. Dinstein, 1973. Textural features for image classification, IEEE Trans. Sys. Man. Cybern., SMC-3: 610-621
  10. Herold, M. H, X. Liu, and K. C. Clake, 2003. Spatial Metrics and Image Texture for Mapping Urban Land Use, PE&RS, 69: 991-1001
  11. Kiema, J. B. K, 2002. Texture analysis and data fusion in the extraction of topographic object from satellite imagery, Int. Jour. of Remote Sensing, 23(4): 767-776
  12. Maillard, P., 2003. Comparing Texture Analysis Methods through Classification, PE&RS, 69(4): 357-367
  13. Parker, J. R., 1997. Algorithms for Image Processing and Computer Vision, John Wiley & Sons
  14. Smith, A. M. S., M. J. Wooster, A. K. Powell, and D. Usher, 2002. Texture based feature extraction: application to burn scar detection on Earth observation satellite sensor imagery, Int. Jour. Remote Sensing, 23: 1733-1739
  15. Wang, X. and A. R. Hanson, 2001. Surface Texture and microstructure extraction from multiple aerial images, Computer Vision and Image Understanding, 83: 1-37
  16. Zhang, Y., 1999. Optimisation of building detection in satellite images by combining multispectral classification and Texture filtering, ISPRS Journal of Photogrammetry & Remote Sensing, 54: 50-60