Evidential Reasoning in Image Understanding
This work addresses image classification challenges in remote sensing, but it appears incremental as it compares to existing methods without claiming major breakthroughs.
The paper tackled the problem of classifying multispectral remote sensing images by applying evidential reasoning, specifically the Dempster-Shafer approach, to improve contextual classification results compared to methods like Bayesian classification, dynamic programming, and stochastic relaxation.
In this paper, we present some results of evidential reasoning in understanding multispectral images of remote sensing systems. The Dempster-Shafer approach of combination of evidences is pursued to yield contextual classification results, which are compared with previous results of the Bayesian context free classification, contextual classifications of dynamic programming and stochastic relaxation approaches.