대한원격탐사학회지 (Korean Journal of Remote Sensing)

  • 제21권2호
  • /
  • Pages.121-133
  • /
  • 2005
  • /
  • 1225-6161(pISSN)
  • /
  • 2287-9307(eISSN)
대한원격탐사학회 (Korean Society of Remote Sensing)
권호 표지
DOI QR코드

DOI QR Code

GLCM/GLDV 기반 Texture 알고리즘 구현과 고 해상도 영상분석 적용

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

  • 이기원 (한성대학교 정보시스템공학과) ;
  • 전소희 (서울대학교 지구과학교육과) ;
  • 권병두 (서울대학교 지구과학교육과)
  • 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)
  • 발행 : 2005.04.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

화소들 사이의 관계를 고려해 Texture 영상을 생성해 내는 것을 의미하는 Texture 영상화는 유용한 영상 분석 방법 중의 하나로 잘 알려져 있고, 대부분의 상업적인 원격 탐사 소프트웨어들은 GLCM이라는 Texture 분석 기능을 제공하고 있다. 본 연구에서는, GLCM 알고리즘에 기반한 Texture 영상화 프로그램이 구현되었고, 추가적으로 GLDV에 기반을 둔 Texture 영상화 모듈 프로그램을 제공한다. 본 프로그램에서는 Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment(ASM), Contrast 등과 같은 GLCN/GLDV의 6가지 Texture 변수에 따라 각각 이에 해당하는 Texture 영상들을 생성해 낸다. GLCM/GLDV Texture 영상 생성에서는 방향 의존성을 고려해야 하는데, 이 프로그램에서는 기본적으로 동-서, 북동-남서, 북-남, 북서-남동 등의 기본적인 방향설정을 제공한다. 또한 이 논문에서 새롭게 구현된 커널내의 모든 방향을 고려해서 평균값을 계산하는 Omni 방향 모드와 커널내의 중심 화소를 정하고_그 주변 화소에 대한 원형 방향을 고려하는 원형방향 모드를 지원한다. 또한 본 연구에서는 여러 가지 변수와 모드에 따라 얻어진 Texture 영상의 분석을 위하여 가상 영상 및 실제 위성 영상들에 의하여 생성된 Texture 영상간의 특징 분석과 상호상관 분석을 수행하였다. Texture 영상합성 응용시에는 영상의 생성시에 적용된 변수들에 대한 이해와 영상간의 상관도를 분석하는 과정이 필요할 것으로 생각된다.

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.

키워드

참고문헌

  1. Al-Janobi, A., 2001. Performing evaluation of cross- diagonal Texture matrix method of Texture analysis, Pattern Recognition, 34: 171-180 https://doi.org/10.1016/S0031-3203(99)00206-X
  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 https://doi.org/10.1016/j.chemolab.2004.02.005
  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 https://doi.org/10.1016/S0098-3004(03)00089-X
  4. Cooper, G. R. J., 2004. The Texture analysis of gravity data using co-occurrence matrices, Computer & Geosciences, 30: 107-115 https://doi.org/10.1016/j.cageo.2003.08.001
  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. ucalgary.ca/~mhallbey/Texture/Texture_tuto 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 https://doi.org/10.1109/TSMC.1973.4309314
  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 https://doi.org/10.14358/PERS.69.9.991
  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 https://doi.org/10.1080/01431160010026005
  12. Maillard, P., 2003. Comparing Texture Analysis Methods through Classification, PE&RS, 69(4): 357-367 https://doi.org/10.14358/PERS.69.4.357
  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 https://doi.org/10.1080/01431160110106104
  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 https://doi.org/10.1006/cviu.2001.0916
  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 https://doi.org/10.1016/S0924-2716(98)00027-6