A NEW ADAPTIVE CLASSIFICATION FUSION METHOD BASED ON DEMPSTER-SHAFER THEORY IN REMOTE SENSING IMAGE

Abstract

In this paper, a new adaptive classification fusion method was proposed based on the Dempster-Shafer's theory of evidence and fuzzy Kohonen neural network in remote sensing image. The new method incorporates ideas from unsupervised neural network model and uses neighborhood information in the framework of the Dempster-Shafer theory of evidence. This approach mainly consists in considering each neighbor of a pattern to be classified as an item of evidence supporting certain hypotheses concerning the class membership of that pattern. This evidence is represented by basic probability assignment, with pooled utilization of the Dempster's rule of combination. Experiments with SPOT remote sensing image demonstrate the excellent performance of this classification scheme as compared with the existing neural network techniques.