Hybrid consensus theoretic classification

Hybrid classification methods based on consensus from several data sources are considered. Each data source is at first treated separately and modeled using statistical methods. Then weighting mechanisms are used to control the influence of each data source in the combined classification. The weights are optimized in order to improve the combined classification accuracies. Both linear and nonlinear optimization methods are considered and used in classification of two multisource remote sensing and geographic data sets. A nonlinear method which utilizes a neural network gives excellent experimental results. The hybrid statistical/neural method outperforms all other methods in terms of test accuracies in the experiments.

[1]  Daniel Graupe.  Time series analysis: Identification and adaptive filtering , 1984 .

[2]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[3]  R. M. Hoffer.  Computer-aided analysis of Skylab multispectral scanner data in mountainous terrain for land use, forestry, water resource, and geologic applications , 1975 .

[4]  R. L. Winkler.  Combining Probability Distributions from Dependent Information Sources , 1981 .

[5]  J. Franklin,et al.  Coniferous Forest Classification and Inventory Using Landsat and Digital Terrain Data , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[7]  Gail A. Carpenter,et al.  ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain data , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[8]  Hakil Kim,et al.  Evidential reasoning approach to multisource-data classification in remote sensing , 1995, IEEE Trans. Syst. Man Cybern..

[9]  Sung-Bae Cho,et al.  Multiple network fusion using fuzzy logic , 1995, IEEE Trans. Neural Networks.

[10]  John Zelek,et al.  The CCRS SAR/MSS Anderson River Data Set , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Ren C. Luo,et al.  Multisensor integration and fusion in intelligent systems , 1989, IEEE Trans. Syst. Man Cybern..

[12]  T. Logan,et al.  Improving forest cover classification accuracy from Landsat by incorporating topographic information , 1978 .

[13]  Robert A. Jacobs,et al.  Methods For Combining Experts' Probability Assessments , 1995, Neural Computation.

[14]  Philip H. Swain,et al.  Multisource Data Analysis in Remote Sensing and Geographic Information Systems Based on Shafer's Theory of Evidence , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[15]  Anil K. Jain,et al.  A Markov random field model for classification of multisource satellite imagery , 1996, IEEE Trans. Geosci. Remote. Sens..

[16]  P. Swain,et al.  Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data , 1990 .

[17]  Jon Atli Benediktsson,et al.  Consensus theoretic classification methods , 1992, IEEE Trans. Syst. Man Cybern..

[18]  A. Jones,et al.  Use of digital terrain data in the interpretation of SPOT-1 HRV multispectral imagery , 1988 .

[19]  Laveen N. Kanal,et al.  Consensus Rules , 1985, UAI.

[20]  Bernard W. Silverman,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[21]  V. Judson Harward,et al.  Mapping forest vegetation using landsat TM imagery and a canopy reflectance model , 1994 .

[22]  Christian Genest,et al.  Combining Probability Distributions: A Critique and an Annotated Bibliography , 1986 .

[23]  Anil K. Jain,et al.  Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images , 1994, IEEE Trans. Geosci. Remote. Sens..

[24]  A. Srinivasan,et al.  Analysis of GIS Spatial Data Using Knowledge-Based Methods , 1993, Int. J. Geogr. Inf. Sci..

[25]  David A. Landgrebe,et al.  A means for utilizing ancillary information in multispectral classification. [of remotely sensed data , 1982 .

[26]  Tong Lee,et al.  Probabilistic and Evidential Approaches for Multisource Data Analysis , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Jon Atli Benediktsson,et al.  Statistical methods and neural network approaches for classification of data from multiple sources , 1991 .

[28]  Etienne Barnard,et al.  Optimization for training neural nets , 1992, IEEE Trans. Neural Networks.