Evidence Theory and Its Applications
Evidence Theory and Its Applications
On Clustering Biological Data Using Unsupervised and Semi-Supervised Message Passing
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
A Semi-Supervised Learning Method for Remote Sensing Data Mining
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Multi-scale data fusion using Dempster-Shafer evidence theory
Integrated Computer-Aided Engineering
Using Dempster---Shafer to incorporate knowledge into satellite image classification
Artificial Intelligence Review
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The image classification process is based on the assumption that pixels which have similar spatial distribution patterns, or statistical characteristics, belong to the same spectral class. In a previous study we have shown how we can improve the accuracy of classification of remotely sensed imagery data by incorporating contextual elevation knowledge in a form of a digital elevation model with the output of the classification process using Dempster-Shafer Theory of Evidence. A knowledge based approach is created for this purpose using suitable production rules derived from the elevation distributions and range of values for the elevation data attached to a particular satellite image. Production rules are the major part of knowledge representation and have the basic form: IF condition THEN Inference. Although the basic form of production rules has shown accuracy improvement, in general, in some cases accuracy can degrade. In this paper we propose a "refined" approach that takes into account the actual "distribution" of elevation values for each class rather than simply the "range" of values to solve the accuracy degradation. This approach is performed by refining the basic production rules used in the previous study taking into account the number of pixels at each elevation within the elevation distribution for each class.