Multicovariance Matched Filter for Target Detection in Images PDF Download
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Author:
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ISBN:
Category :
Languages : en
Pages : 9
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Book Description
Our original research on the multicovariance matched filter deals with optimum low resolution target detection in a single-frame, multicolor image, such as a multispectral infrared or polarimetric synthetic aperture radar picture. The multicovariance method completely uses all the joint variability of the problem, in both space and frequency, in a way that generalizes both the traditional spatial matched filter and also techniques involving scalar ratios between frequency bands. The main new focus of our work, directed toward achieving the best target detection performance that is possible, is to develop a preprocessing step involving optimal adaptive estimation of the local clutter background. This involves segmenting the image into regions, which correspond to different background/clutter statistical models. Statistics of real data are being studied and used in new state-of-the-art hierarchical segmentation algorithms based on Markov Random Field, polynomial and autoregressive models for vector-valued random processes. The major algorithmic challenges here are in estimating the best possible background/clutter models and in accurately estimating the boundaries between different model regions. We are in the process of developing extremely efficient and robust algorithms to estimate these clutter models. These are similar to familiar algorithms from mainstream signal processing, but solve the interpolation problem for Markov Random Fields, which is different than the usual linear prediction problem. (MM).
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 9
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Book Description
Our original research on the multicovariance matched filter deals with optimum low resolution target detection in a single-frame, multicolor image, such as a multispectral infrared or polarimetric synthetic aperture radar picture. The multicovariance method completely uses all the joint variability of the problem, in both space and frequency, in a way that generalizes both the traditional spatial matched filter and also techniques involving scalar ratios between frequency bands. The main new focus of our work, directed toward achieving the best target detection performance that is possible, is to develop a preprocessing step involving optimal adaptive estimation of the local clutter background. This involves segmenting the image into regions, which correspond to different background/clutter statistical models. Statistics of real data are being studied and used in new state-of-the-art hierarchical segmentation algorithms based on Markov Random Field, polynomial and autoregressive models for vector-valued random processes. The major algorithmic challenges here are in estimating the best possible background/clutter models and in accurately estimating the boundaries between different model regions. We are in the process of developing extremely efficient and robust algorithms to estimate these clutter models. These are similar to familiar algorithms from mainstream signal processing, but solve the interpolation problem for Markov Random Fields, which is different than the usual linear prediction problem. (MM).
Author: Jason E. West
Publisher:
ISBN:
Category : Detectors
Languages : en
Pages : 192
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Book Description
"Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters, which may be derived in many different scientific fields, can be used to locate spectral targets by modeling scene background as either structured geometric) with a set of endmembers (basis vectors) or as unstructured stochastic) with a covariance matrix. In unstructured background research, various methods of calculating the background covariance matrix have been developed, each involving either the removal of target signatures from the background model or the segmenting of image data into spatial or spectral subsets. The objective of these methods is to derive a background which matches the source of mixture interference for the detection of sub pixel targets, or matches the source of false alarms in the scene for the detection of fully resolved targets. In addition, these techniques increase the multivariate normality of the data from which the background is characterized, thus increasing adherence to the normality assumption inherent in the matched filter and ultimately improving target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This thesis will establish a strong theoretical foundation, describing the necessary preprocessing of hyperspectral imagery, deriving the spectral matched filter, and capturing current methods of unstructured background characterization. The extensive experimentation will allow for a comparative evaluation of several current unstructured background characterization methods as well as some new methods which improve stochastic modeling of the background. The results will show that consistent improvements over the scene-wide statistics can be achieved through spatial or spectral subsetting, and analysis of the results provides insight into the tradespaces of matching the interference, background multivariate normality and target exclusion for these techniques"--Abstract.
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ISBN:
Category : Aeronautics
Languages : en
Pages : 538
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Author:
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ISBN:
Category :
Languages : en
Pages : 5
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Most detection algorithms for hyperspectral imaging applications assume a targetwith a perfectly known spectral signature. In practice, the target signature is either imperfectly measured (target mismatch) and/or it exhibits spectral variability. The objective of this paper is to introduce a robust matched lter that takes the uncertainty and/or variability of target signatures into account. It is shown that, if we describe this uncertainty with an ellipsoid in the spectral space, we can design a matched lter that provides a response of the same magnitude for all spectra within this ellipsoid. Thus, by changing the size of this ellipsoid, we can control the "spectral selectivity" of the matched lter. The ability of the robust matched lter to deal effectively with target mismatch and spectral variability is demonstrated with hyperspectral imaging data from the HYDICE sensor.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 6
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Book Description
Target deteetion in reflective hyperspeetral imagery generally involves the application of a spectral matched filter on a per-pixei basis to create an image of the target lihenhood of occupying each pixel. Stochastic (or unstructured) target detection tcehniques require the user to define an estimate of the background mean and covariance from which to separate out the desired targets in the image. Typically, scene-wide statistics are used, although it Is simple to show that this methodology does not produce sufficiently multivariate normal bachgrounds nor does it necessarily represent the best suppression of likely false alarms.
Author:
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ISBN:
Category : Electrical engineering
Languages : en
Pages : 1360
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Author: Yeunnung Chen
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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The optimal detection of a three-dimensional moving target calls for the classical technique of matched filtering. If a target is modeled as a moving point source with unknown velocity, then the velocity alone determines the shape of the observed signal. Thus, target velocity is a parameter that completely characterizes the matched filter. We will designate these types of matched filters to be the assumed velocity filters (AVFs) to emphasize the velocity parameter. Like most matched filtering techniques where the signal parameters range in a continuum, the AVF must be implemented suboptimally by quantizing the velocity space. In this report we will use a loss factor that measures the average loss of signal-to-noise ratio (SNR) at the output of the matched filter due to mismatch of filter parameters. The loss factor can be used as a criterion for partitioning the velocity space. We will show that, with a fixed loss factor, the number of filters required for coverage increases linearly as the span of the two-dimensional velocity space increases quadratically. The rate of increase is further reduced when the loss factor is made proportional to expected target angular speed.
Author: Samuel S. Blackman
Publisher: Artech House Publishers
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 1306
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Book Description
Here's a thorough overview of the state-of-the-art in design and implementation of advanced tracking for single and multiple sensor systems. This practical resource provides modern system designers and analysts with in-depth evaluations of sensor management, kinematic and attribute data processing, data association, situation assessment, and modern tracking and data fusion methods as applied in both military and non-military arenas.
Author:
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ISBN:
Category : Science
Languages : en
Pages : 1578
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Author: Adam Cisz
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 262
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"This thesis performs a performance comparison on existing hyperspectral target detection algorithms. The algorithms chosen for this analysis include multiple adaptive matched filters and the physics based modeling invariant technique. The adaptive matched filter algorithms can be divided into either structured (geometrical) or unstructured (statistical) algorithms. The difference between these two categories is in the manner in which the background is characterized. The target detection procedure includes multiple pre-processing steps that are examined here as well. The effects of atmospheric compensation, dimensionality reduction, background characterization, and target subspace creation are all analyzed in terms of target detection performance. At each step of the process, techniques were chosen that consistently improved target detection performance. The best case scenario for each algorithm is used in the final comparison of performance. The results for multiple targets were computed and statistical matched filter algorithms were shown to outperform all others in a fair comparison. This fair comparison utilized a FLAASH atmospheric compensation for the matched filters that was equivalent to the physics based invariant process. The invariant technique was shown to outperform the geometric matched filters that it uses in its approach. Each of these techniques showed improvement over the SAM algorithm for three of the four targets analyzed. Multiple theories are proposed to explain the anomalous results for the most difficult target"--Abstract.
