Multi-sensor data fusion for supervised land-cover classification using Bayesian and geostatistical techniques

被引：0

作者：

Park N.-W. ^{[1
]}

Moon W.M. ^{[2
,3
]}

Chi K.-H. ^{[1
]}

Kwon B.-D. ^{[4
]}

机构：

[1] National Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources, Yusung-ku, Daejeon 305-350

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

[3] Geophysics, University of Manitoba, Winnipeg

[4] Department of Earth Science Education, Seoul National University

来源：

Geosciences Journal | 2002年 / 6卷 / 3期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Bayesian; Classification; Data fusion; Indicator kriging; Smoothed kernel;

D O I：

10.1007/BF02912690

中图分类号：

学科分类号：

摘要：

We propose a geostatistical approach incorporated to the Bayesian data fusion technique for supervised classification of multi-sensor remote sensing data. The classification based only on the traditional spectral approach cannot preserve the accurate spatial information and can result in unrealistic classification results. To obtain accurate spatial/contextual information, the indicator kriging that allows one to estimate the probability of occurrence of certain classes on the basis of surrounding pixel information is incorporated into the Bayesian framework. This new approach has its merit incorporating both the spectral information and spatial information and improves the confidence level in the final data fusion task. To illustrate the proposed scheme, supervised classification of multi-sensor test remote sensing data was carried out. Analysis of the results indicates that the proposed method considerably improves the classification accuracy, compared to the methods based on the spectral information alone.

引用

页码：193 / 202

页数：9

共 24 条

[1]

Choi S.W., Moon W.M., Choi S.K., Fuzzy logic fusion of W - Mo exploration data from Seobyeog-ri, Korea, Geosciences Journal, 4, pp. 43-52, (2000)

[2]

Chung C.F., Fabbri A.G., Probabilistic prediction models for landslide hazard mapping, Photogrammetric Engineering & Remote Sensing, 65, pp. 1389-1399, (1999)

[3]

Duda R.O., Hart P.E., Stork D.G., Pattern Classification, (2000)

[4]

Giacinto G., Roli F., Bruzzone L., Combination of neural and statistical algorithms for supervised classification of remote-sensing images, Pattern Recognition Letters, 21, pp. 385-397, (2000)

[5]

Goovaerts P., Comparison of CoIK, IK and mIK performances for modeling conditional probabilities of categorical variables, Geostatistics for the next Century, pp. 18-29, (1994)

[6]

Goovaerts P., Geostatistics for Natural Resources Evaluation, (1997)

[7]

Goovaerts P., Geostatistical incorporation of spatial coordinates into supervised classification of hyperspectral data, Journal of Geographical Systems, 4, pp. 99-111, (2002)

[8]

Hall D.L., Mathematical Techniques in Multisensor Data Fusion, (1992)

[9]

Isaaks E.H., Srivastava R.M., An Introduction to Applied Geostatistics, (1989)

[10]

Lee T., Richards J.A., Swain P.H., Probabilistic and evidential approaches for multi-source data analysis, IEEE Transactions on Geoscience and Remote Sensing, 25, pp. 283-292, (1987)

← 1 2 3 →