A Dempster-Shafer Relaxation Approach to Context Classification
Date
2007
Authors
Richards, John
Jia, Xiuping
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
A relaxation scheme is proposed in which Dempster-Shafer evidential theory is used to bring the effect of the spatial neighborhood of a pixel into a classification. The benefits include the ability to incorporate uncertainty in the neighborhood information, allowing a stopping criterion to be devised based on increasing the uncertainty contribution of the neighborhood to unity within a prescribed number of iterations. The number of iterations to be used is governed by several factors, including an estimate of how far out in the neighborhood pixels are assumed to be influential. As with standard relaxation labeling, but unlike many other context-sensitive methods, the evidential approach can be initialized from the results of a separate point statistical classification of the image; it is also consistent with multisource analyses based on evidential methods for fusion. A variation of evidential relaxation using considerably simplified neighborhood information is also developed, illustrating that very good results can be obtained without detailed knowledge of the spatial properties of a scene. The new procedures are compared experimentally with standard probabilistic relaxation and the application of Markov random fields.
Description
Keywords
Keywords: Dempster Shafer; Evidential theory; Markov random fields (MRF); Spatial context; Thematic mapping; Conformal mapping; Iterative methods; Markov processes; Pixels; Statistical methods; Uncertainty analysis; Image classification Dempster-Shafer; Evidence; Markov random fields (MRFs); Relaxation; Spatial context; Thematic mapping
Citation
Collections
Source
IEEE Transactions on Geoscience and Remote Sensing
Type
Journal article
Book Title
Entity type
Access Statement
License Rights
Restricted until
2037-12-31