The multi-sensor fusion can provide more accurate and reliable information compared to information from each sensor separately taken. Moreover, the data from multiple heterogeneous sensors present in the medical surveillance systems have different degrees of uncertainty. Among multisensor data fusion techniques, Bayesian methods and evidence theories such as Dempster-Shafer Theory (DST), are co...

We study two related topics in decision fusion for multisensor image classification. The first topic is the use of a weighted logarithmic opinion pool compared to the statistical product combination rule. The performance is compared on three data sets. The second topic is related to different criteria for parameter estimation for a statistical fusion model. We propose an alternative criterion f...

The latest developments in Markov models’ theory and their corresponding computational techniques have opened new rooms for image and signal modeling. In particular, the use of Dempster–Shafer theory of evidence within Markov models has brought some keys to several challenging difficulties that the conventional hidden Markov models cannot handle. These difficulties are concerned mainly with two...

Multisensor data fusion is very important in defence systems. Data fusion can be used to integrate individual sensor data into the common operational picture of the battlefield. However, there is still a possibility to improve the quality of individual sensors. In this paper, we investigate a possibility to improve the quality of individual sensor data from Electronic Warfare (EW) systems. Some...

The majority of optical observations acquired via spaceborne Earth imagery are affected by clouds. While there is numerous prior work on reconstructing cloud-covered information, previous studies are, oftentimes, confined to narrowly defined regions interest, raising the question whether an approach can generalize a diverse set at variable cloud coverage or in different and seasons. We target c...

The multi-sensor fusion can provide more accurate and reliable information compared to information from each sensor separately taken. Moreover, the data from multiple heterogeneous sensors present in the medical surveillance systems have different degrees of uncertainty. Among multi-sensor data fusion techniques, Bayesian methods and evidence theories such as Dempster-Shafer Theory (DST), are c...