| This paper attacks the problem of generalized multisensor mixture estimation. A distribution mixture is said to be generalized when the exact nature of components is not known, but each of them belongs to a nite known set of families of distributions. Estimating such a mixture entails a supplementary diiculty: one must label, for each class and each sensor, the exact nature of the correspondi...

We describe an approach for registration of images from diierent sensors that may generate local polarity reversals and inconsistent features. The developed technique is based on modifying the objective function by transforming the images into a representation that is less dependent on the local image intensity, and using an iterative process that reduces the eeect of inconsistent features.

Multisensor data fusion and integration is a rapidly evolving research area that requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. Multisensor data fusion refers to the synergistic combination of sensory data from multiple sensors and related information to provide more reliable and accurate information than coul...

Most change detection (CD) methods assume that prechange and postchange images are acquired by the same sensor. However, in many real-life scenarios, e.g., natural disasters, it is more practical to use latest available before after occurrence of incidence, which may be using different sensors. In particular, we interested combination optical synthetic aperture radar (SAR) SAR appear vastly fro...

An algorithm for multiresolutional multisensor target identiication is presented. It uses the computationally eecient scale sequential approach to hypothesis testing during identiication and implements the inverse discrete wavelet transform to fuse data from diierent multires-olution sensors. We show that fusion leads to an increase in the probability of correct identiication without a signiica...

Multisensor data fusion has found widespread application in industry. The objective of data fusion is to provide an improved estimate of a state from a set of data provided by different sensors. Among the various multisensor approaches, Kalman filtering is one of the most significant. Reliability of the data provided by the sensor is a key factor for the integrity of the fusion process. Kalman ...

In this study, a fuzzy-based multisensor data fusion classifier is developed and applied to land cover classification using ERS-1/JERS-1 SAR composites. This classifier aims at the integration of multisensor and contextual information in a single and a homogeneous framework. Initial Fuzzy Membership Maps (FMM) to different thematic classes are first calculated using classes and sensors a priori...

In this paper we propose a novel fault tolerant multisensor switching strategy for feedback control. Each sensor of the proposed multisensor scheme has an associated state estimator which, together with a state feedback gain, is able to individually stabilise the closed loop system. At each instant of time, the switching strategy selects the sensor-estimator pair that provides the best closed l...