Lithological discrimination of Altun area in northwest China using Landsat TM data and geostatistical textural information

被引：4

作者：

Li P. ^{[1
,2
]}

Li Z. ^{[1
]}

Moon W.M. ^{[2
]}

机构：

[1] Institute of Remote Sensing and GIS, Peking University

[2] ESI3 Laboratory, School of Earth and Environmental Sciences, Seoul National University

来源：

Geosciences Journal | 2001年 / 5卷 / 4期

关键词：

Digital classification; Lithological discrimination; Rodogram; Texture; Variogram;

D O I：

10.1007/BF02912700

中图分类号：

学科分类号：

摘要：

The image texture was extracted from Landsat TM data using rodogram, a geostatistical function, and then added to multispectral classification for lithological discrimination of an arid region, the Altun Mountains in northwest China. The variogram analysis of the image of the study area indicates that the image has two scales of textures: local and regional textures. Therefore, two different window sizes, 17x17 pixels and 61x61 pixels were chosen to extract textural information using rodogram. The results of image classification show that the classification based on spectral data and geostatistical textural information produced much higher overall accuracy than with the spectral classification alone. Moreover, large window size, at which textural information was extracted and then used for image classification, achieved more accurate result than small window size.

引用

页码：293 / 300

页数：7

共 28 条

[1]

An P., Chung C.F., Renz A.N., Digital lithology mapping from airborne geophysical and remote sensing data in the Melville Peninsula, northern Canadian using a neural network approach, Remote Sensing of Environment, 53, pp. 76-84, (1995)

[2]

Berberoglu S., Lloyd C.D., Atkinson P.M., Curran P.J., The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean, Computers & Geosciences, 26, pp. 385-396, (2000)

[3]

Carr J.R., Spectral and textural classification of single and multiple band digital images, Computers & Geosciences, 22, pp. 849-865, (1996)

[4]

Carr J.R., Miranda F.P., The semivariogram in comparison to the co-occurrence matrix for classification of image texture, IEEE Transactions on Geoscience and Remote Sensing, 36, pp. 1945-1952, (1998)

[5]

Chica-Olmo M., Arbarca-Hernandez F., Computing geostatistical image texture for remotely sensed data classification, Computers & Geosciences, 26, pp. 373-383, (2000)

[6]

Cressie N., Hawkins D.M., Robust estimation of the variogram, 1, Journal of the International Association for Mathematical Geology, 12, pp. 115-125, (1980)

[7]

Curran P.J., The semivariogram in remote sensing: An introduction, Remote Sensing of Environment, 24, pp. 493-507, (1988)

[8]

Deutsch C.V., Journel A.G., GSLIB: Geostatistical Software Library and Users Guide, pp. 39-60, (1992)

[9]

Emran A., Hakdaoui M., Chorowicz J., Anomalies on geologic maps from multispectral and textural classification: The Bleida mining district (Morocco), Remote Sensing of Environment, 57, pp. 13-21, (1996)

[10]

Glaseby C.A., Normal distribution assumptions in discrimination, Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS 88), pp. 1789-1791, (1988)

← 1 2 3 →