Methods for multisource data analysis in remote sensing

Methods for classifying remotely sensed data from multiple data sources are considered. Special interest is in general methods for multisource classification and three such approaches are considered: Dempster-Shafer theory; fuzzy set theory; and statistical multisource analysis. To apply statistical multisource analysis successfully it is necessary to characterize the reliability of each data source. Separability measures and classification accuracy are used to measure the reliability. These reliability measures are then associated with reliability factors included in the statistical multisource analysis to multispectral scanner data where different segments of the electromagnetic spectrum are treated as different sources. A discussion is included concerning future directions for investigating reliability measures.

[1]  H. Carter.  Fuzzy Sets and Systems — Theory and Applications , 1982 .

[2]  John A. Richards,et al.  Multisource Data Analysis in Remote Sensing and Geographic Information Processing , 1985 .

[3]  Robert J. McEliece,et al.  The Theory of Information and Coding , 1979 .

[4]  K. S. Fu,et al.  AN AXIOMATIC APPROACH TO RATIONAL DECISION MAKING IN A FUZZY ENVIRONMENT , 1975 .

[5]  Mitsuru Ishizuka,et al.  Inference procedures under uncertainty for the problem-reduction method , 1982, Inf. Sci..

[6]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[7]  John D. Lowrance,et al.  An Inference Technique for Integrating Knowledge from Disparate Sources , 1981, IJCAI.

[8]  Mohan M. Trivedi,et al.  Low-Level Segmentation of Aerial Images with Fuzzy Clustering , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  J. Berger.  Statistical Decision Theory and Bayesian Analysis , 1988 .

[10]  M. Goldberg,et al.  A hierarchical expert system for updating forestry maps with Landsat data , 1985, Proceedings of the IEEE.

[11]  Mohan Trivedi,et al.  Segmentation of a Thematic Mapper Image Using the Fuzzy c-Means Clusterng Algorthm , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Jeffrey A. Barnett,et al.  Computational Methods for a Mathematical Theory of Evidence , 1981, IJCAI.

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

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