1. 北京大学遥感与地理信息系统研究所, 北京 100871;
2. 普渡大学土木工程系, 美国印第安纳州 IN 47907-1284
THE APPLICATION OF GEOSTATISTICAL IMAGE TEXTURE TO REMOTE SENSING LITHOLOGICAL CLASSIFICATION
HUANG Ying-duan1, LI Pei-jun1, LI Zheng-xiao2
1. Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China;
2. Geomatic Engineering, School of Civil Engineering, Purdue University, IN 47907-1284 U.S.A.
Abstract：The texture is one of the important features of remote sensing images. In this paper, the image texture was extracted from Landsat TMdata by means of semivariogram of logarithms, one of the geostatistic functions, and added to multispectral lithological classification. Different window sizes were used to extract textural information. The results of image classification show that the classification based on spectral data and geostatistical textural information can produce much higher overall accuracy than that based merely on spectral data. Moreover, for lithological discrimination based on multispectral data, the larger the window size for texture extraction is, the more accurate the classification result will become. In practice, however, other factors, such as the boundary effect and the accuracy of some important lithological units, need to be considered in choosing an appropriate window size.
收稿日期: 2003-01-06
出版日期: 2011-08-02
基金资助:
教育部留学回国人员科研启动基金资助项目。
作者简介: 黄颖端(1977-),女,北京大学硕士研究生,研究方向为遥感信息处理.
引用本文:
黄颖端, 李培军, 李争晓. 基于地统计学的图像纹理在岩性分类中的应用[J]. 国土资源遥感, 2003, 15(3): 45-49.
HUANG Ying-duan, LI Pei-jun, LI Zheng-xiao. THE APPLICATION OF GEOSTATISTICAL IMAGE TEXTURE TO REMOTE SENSING LITHOLOGICAL CLASSIFICATION. REMOTE SENSING FOR LAND & RESOURCES, 2003, 15(3): 45-49.
[1] Haralick R M, Shanmugam K, Dinstein I.Texture feature for image classification［J］.IEEE Transactions on Systems, Man, and Cybermetics, 1973, 3: 610-621.[2] Ramstein G, Raffy M.Analysis of the structure of radiometric remotely-sensed images［J］.International Journal of Remote Sensing, 1989, 10: 1049-1073.[3] Miranda F P, Carr J R.Application of the semivariogram texture classifier (STC) for vegetation discrimination using SIRB data o the Guiana Shield, northwestern Brazil［J］.Remote Sensing Reviews, 1994, 10: 155-168.[4] Lark R M.Geostatistical description of texture on an aerial photograph for discriminating classes of land cover［J］.International Journal of Remote Sensing, 1996, 17: 2115-2133.[5] Carr J R, Miranda F P.The semivariogram in comparison to the co-occurrence matrix for classification of image texture［J］.IEEE Transactions on Geoscience and Remote Sensing, 1998, 36: 1945-1952.[6] Miranda F P, Fonseca L E N, Carr J R.Semivariogram textural classification of JERS-1 (Fuyo-1) SAR data obtained over a flooded area of the Amazon rainforest［J］.International Journal of Remote Sensing, 1998, 19: 549-556.[7] Chica-Olmo M, Abarca-Hernandez F.Computing geostatistical image texture for remotely sensed data classification［J］.Computers & Geosciences, 2000, 26: 373-383.[8] Berberoglu S, Lloyd C D, Atkinson P M, et al.The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean［J］.Computers & Geosciences, 2000, 26: 385-396.[9] Woodcock C E, Harward V J.Nested-hierarchical scene models and image segmentation［J］.International Journal of Remote Sensing, 1992, 13: 3167-3187.[10] Curran P J, Atkinson P M.Geostatistic & Remote Sensing［J］.Progress in Physical Geography, 1998, 22(1): 61-78.[11] Abarca-Hernandez F, Chica-Olmo M. Evaluation of geostatisical measures of radiometric spatial variability for lithologic discrimination in Landsat TM images［J］.Photogrammetric Engineering and Remote Sensing,1999, 65(6): 705-711.[12] Goovaerts P.Geostatistics for Natural Resources Evaluation［M］.New York: Oxford University Press, 1997.[13] Deutsch C V, Journel A G.GSLIB: geostatistical software library and users guide［M］.New York: Oxford University Press, 1992.[14] Story M, Congalton R G.Accuracy Assessment: A User's Perspective［J］.Photogrammetric Engineering and Remote Sensing, 1986, 52(3): 397-399.
2003, Vol. 15 
