Applying the SAM Supervised Classification Algorithm to Prepare a Map of Rock Units Using Satellite Images

Document Type : علمی - پژوهشی


1 Associate Prof., Dep. of Remote Sensing and GIS, Kharazmi University of Tehran

2 M.Sc. of Remote Sensing and GIS, School of Geography, University of Kharazmi

3 Expert and Researcher of Jahad Daneshgahi, Lorestan

4 Bachelor of Civil Engineering, Faculty of Engineering, University of Khorramabad


The issue of the mapping of rock units in an ever-expanding area has now reached a point where the detection and classification of rock units is carried out through the aid of hyperspectral image. In this research, Hyperion images are used in the light of the work of previous researchers and the application of the SAM supervised classification algorithm for the detection and separation of rock units in Khorramabad region, Lorestan province. After performing the necessary preprocesses including atmospheric correction performed by the FLAASH method, linear MNF transformation was used to determine the dimension of main image, to separate the noise from other information and reduce the processing level in the next stages, and the PPI algorithm to find the pixels that More purity is used in multispectral images. From the overlapping of pure pixels with rock units and based on ground data from the study area, the average range was extracted for each member. Then, these pure members were used as inputs for the above-mentioned algorithms and image categorization was used. Finally, the mapped classification of this method was compared with existing maps and land data and their accuracy was checked. The accuracy of the SAM method was verified by verifying the accuracy of the algorithm by calculating the error matrix with the highest of 68.83% and kappa coefficient of 0.49%, which indicates the importance of hyperspectral images and the SAM method in separating the rock units.


  1. آزادبخت، س.، 1382، سنگچینهنگاری و زیستچینهنگاری سازند سورگاه در شرق خرم‌آباد (زاگرس)، پایان‌نامة کارشناسی ارشد، دانشگاه پیام نور تهران، دانشکده علوم پایه، گروه چینه‌شناسی و فسیل‌شناسی، تعداد صفحات 135.
  2. رنگزن، ک.، صابری، ع.، جوکار، ا.، محمدیان، ف.، 1390، شناسایی و تخمین سطح زیرکشت اراضی کشاورزی با استفاده از داده‌های بهنگام سنجندة هایپرون، همایش ژئوماتیک90.
  3. سیدین، ع.، 1394، اکتشاف چشمه‌های نفتی (هیدروکربنی) با استفاده از روش‌های آشکارسازی هدف در تصاویر ابرطیفی، پایان‌نامة کارشناسی ارشد، دانشگاه صنعتی خواجه‌نصیرالدین طوسی، دانشکدة نقشه‌برداری.
  4. شریفی، ا.ر.، 1387، طبقه‌بندی تصاویر ابرطیفی ازطریق تجزیه و تحلیل امضای طیفی پدیدهها، پایان‌نامة کارشناسی ارشد، رشتة سـنجش از دور و GIS، دانشگاه تهران.
  5. علوی‌پناه، س.ک.، 1392، کاربرد سنجش از دور در علوم زمین، تهران، مؤسسة انتشارات دانشگاه تهران، چاپ چهارم.
  6. فاطمی، س.ب.، رضایی، ی.، 1385، مبانی سنجش از دور، انتشارات آزاده.
  7. فهیم‌نژاد، ح.، 1386، ارزیابی تفکیک نوع محصولات کشاورزی با استفاده از داده‌های سنجش از دور (سنجندة فراطیفی هایپریون)، پایان‌نامة کارشناسی ارشد، دانشگاه صنعتی خواجه نصیرالدین طوسی، دانشکدة نقشه‌برداری.
  8. نقشة زمینشناسی خرمآباد، مقیاس 1:25000، سازمان زمین‌شناسی و اکتشافات معدنی کشور.
  9. Barry, P., 2001, EO-1/ Hyperion Science Data User‘s Guide, Level 1_B, TRW Space, Defense & Information Systems.
  10. Beck, R., 2003, EO-1 User Guide, Ed. University of Cincinnati, PP. 1–74.
  11. Boardman, J.W., 1994, Automating Spectral Unmixing of AVIRIS Data Using Convexgeometry Concepts, In :Summaries of the 4th Annual JPL Air borne Geoscience Workshop, Pasadena, PP. 11–14.
  12. Camps-Valls, G., Tuia, D., Bruzzone, L. & Benediktsson, J.A., 2014, Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods, IEEE Signal Processing Magazine, 31(1), PP. 45–54.
  13. Chang, C.I., 2003, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Orlando, FL: Kluwer Academic.
  14. Chen, X., Warner, T.A. & Campagna, D.J., 2007, Integrating Visible, Near-Infrared and Short-Wave Infrared Hyperspectral and Multispectral Thermal Imagery for Geological Mapping at Cuprite, Nevada, Remote Sensing of Environment, 110(3), PP. 344–356.
  15. Folkman, M., Pearlman, J., Liao, L.B. & Jarecke, P.J., 2001, EO-1/Hyperion Hyperspectral Imager Design, Development, Characterization, and Calibration, in Second International AsiaPacific Symposium on Remote Sensing of the Atmosphere, Environment, and Space, PP. 40–51.
  16. Goodenough, D., Niemann, K.O., Pearlman, J.S., Hao, C., Han, T., Murdoch, M. & West, C., 2003, Processing Hyperion and ALI for Forest Classification, Geoscience and Remote Sensing, IEEE Transactions on, Vol. 41, PP. 1321–1331.
  17. Hubbard, B.E., Crowley, J.K. & Zimbelman, D.R., 2003, Comparative Alteration Mineral Mapping Using Visible to Shortwave Infrared (0.4–2.4 µm) Hyperion, ALI, and ASTER Imagery, Geoscience and Remote Sensing, Vol. 41, No. 6, PP. 1401–1410.
  18. Kruse, F., Lefkoff, A., Boardman, J., Heidebrecht, K., Shapiro, A., Barloon, P. & Goetz, A., 1993, The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging Spectrometer Data, Remote Sensing of Environment, Vol. 44, Issues 2–3, PP. 145–163.
  19. Kurse, F.A., Boardman, J.W. & Huntington, J.F., 2003, Comparison of Airborne Hyperspectral Data and EO-1 Hyperion for Mineral Mapping, IEEE Transactions on Geoscience and Remote Sensing, No. 41, PP. 1388–1400.
  20. Manolakis, D. & Marden, G.A., 2003, Shaw,―Hyperspectral Image Processing for Automatic Target Detection Applications, Lincoln Laboratory Journal, 14, PP. 154–176.
  21. Oommen, T., Misra, D., Twarakavi, N.K., Prakash, A., Sahoo, B. & Bandopadhyay, S., 2008, An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing, Mathematical Geosciences, 40(4), PP. 409–424.
  22. Rajendran, S., Srinivasamoorthy, K. & Aravindan, S., 2007, Mineal Exploration: Recent Strategies, New India Publishing.
  23. Richards, J.A. & Jia, X., 1999, Remote Sensing Digital Image Analysis–Springer, Berlin, Germany.
  24. Shrestha, D.P., Margate, D.E., Van der Meer, F. & Anh, H.V., 2005, Analysis and Classification of Hyperspectral Data for Mapping Land Degradation: An Application in Southern Spain, International Journal of Applied Earth Observation and Geoinformation, 7(2), PP. 85–96.
  25. Van Deer Meer, F.D. & De Jong Steven, M., 2001, Imagins Spectrometry: Basic Principle and Prospective Applications, Academic press.
  26. Villa, A., Benediktsson, J.A., Chanussot, J. & Jutten, J., 2011, Hyperspectral Image Classification with Independent Component Discriminant Analysis, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 12, PP. 4865–4876.