Training classifiers individually, and then fusing their results, has the potential to improve classification accuracy; often,
dramatic improvements are realized. In this paper we examine how training classifiers using multiple polarimetric
features such as the Cloude-Pottier decomposition, even and odd bounce and the Polarimetric Whitening filter and then
fusing their results affects performance of ship classification. We explore and compare two currently competing
technologies of classifier bagging and classifier boosting for classifier fusion and introduce a new approach which
conducts a search through solution space to configure an optimal classifier given a library of classifiers and features. A
related and important facet of this work is feature selection and feature reduction methods. We explore how the selection
of different features affects classification performance. We also explore estimates of the classifier error and provide
estimates for noise bounds on the data and compare performance of the different methods compared to the noise present
in data.
In this paper we present a distributed processing system that will be used for manual and/or automated change detection using data from databases and from online sensors. The automated change detection algorithm described herein is based on a method developed by Armenakis et al. This technique is applied on two level image classification. Its extension to multiple level classification and change detection is also being discussed. The paper presents two other change detection methodologies that are based on the Principal Component Analysis and wavelet techniques. Finally, it discusses the effect of matched filters for improving the change detection performance. Experimental results are provided using RADARSAT images which have been registered with the automated registration algorithm of AUG Signals that is currently available under the distributed procesing system www.signalfusion.com.
MeteorWatch is a concept for the observation of small meteor events from a microsatellite in low earth orbit. To achieve high spatial resolution (about 1 km), fast update rate (up to 50 Hz), and large instantaneous coverage (107 km2), a distributed sensor is appropriate. The MeteorWatch sensor design has about 300 independent detection modules linked by a data bus to a central controller and image processor. Each detection module has a camera, digitizer, controller, image preprocessor, and bus interface. In operation, each detection module decides on the probability that a particular image has a meteor. Meteor event rates are expected to be low compared to the data rate, so that preprocessing at the detector modules reduces traffic on the data bus to the central controller. Image sequences with probable meteors are sent to the central controller for further processing and extraction of the meteor parameters. This paper gives an overview of MeteorWatch and describes the image processing approach, including partitioning of the tasks between the detection modules and the central image processor, the selection of clutter-rejection algorithms and the limits of detection for small meteors.
In this paper we present a method to obtain a maximum likelihood estimation of the parameters of the Generalized Gamma and K probability density functions. Explicit closed form expressions are derived between the model parameters and the experimental data. Due to their nonlinear nature global optimization techniques are used for solving the derive expressions with respect to clutter model parameters. Experimental results show in all attempted cases that the resulting expressions are convex functions of the parameters. In addition to the maximum likelihood solution we present two other solutions. One is based on moment and the other on histogram matching.
KEYWORDS: Sensors, Target detection, Wavelets, Signal detection, Signal to noise ratio, Radar, Signal processing, Sensor performance, Radar signal processing, Electronic filtering
The objective of this paper is to present a new coherent adaptive Constant False Alarm Rate (CFAR) wavelet detector which can be used as an additional independent detector for effective CFAR detection of point targets. It is shown through examples that this detector may provide a reliable estimate of the clutter mean which in turn is used, when multiplied by a constant, to determine the CFAR detector cutoff point for the target detection process. The detector is coherent and furthermore, the real and imaginary parts of the clutter and target are processed independently and their results are combined. As shown through an experimental example, coherent detectors offer better performance over amplitude detectors at high Signal to Noise Ratios (SNRs). At low SNRs their performance approximates that of amplitude detectors due to the fact that phase information is very sensitive to noise at low SNRs and its contribution becomes insignificant.
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