Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 8 of 8
Full-Text Articles in Signal Processing
Towards The Mitigation Of Correlation Effects In The Analysis Of Hyperspectral Imagery With Extension To Robust Parameter Design, Jason P. Williams
Towards The Mitigation Of Correlation Effects In The Analysis Of Hyperspectral Imagery With Extension To Robust Parameter Design, Jason P. Williams
Theses and Dissertations
Standard anomaly detectors and classifiers assume data to be uncorrelated and homogeneous, which is not inherent in Hyperspectral Imagery (HSI). To address the detection difficulty, a new method termed Iterative Linear RX (ILRX) uses a line of pixels which shows an advantage over RX, in that it mitigates some of the effects of correlation due to spatial proximity; while the iterative adaptation from Iterative Linear RX (IRX) simultaneously eliminates outliers. In this research, the application of classification algorithms using anomaly detectors to remove potential anomalies from mean vector and covariance matrix estimates and addressing non-homogeneity through cluster analysis, both of …
Optimized Hyperspectral Imagery Anomaly Detection Through Robust Parameter Design, Francis M. Mindrup
Optimized Hyperspectral Imagery Anomaly Detection Through Robust Parameter Design, Francis M. Mindrup
Theses and Dissertations
Anomaly detection algorithms for hyperspectral imagery (HSI) are an important first step in the analysis chain which can reduce the overall amount of data to be processed. The actual amount of data reduced depends greatly on the accuracy of the anomaly detection algorithm implemented. Most, if not all, anomaly detection algorithms require a user to identify some initial parameters. These parameters (or controls) affect overall algorithm performance. Regardless of the anomaly detector being utilized, algorithm performance is often negatively impacted by uncontrollable noise factors which introduce additional variance into the process. In the case of HSI, the noise variables are …
Abstracting Gis Layers From Hyperspectral Imagery, Torsten E. Howard
Abstracting Gis Layers From Hyperspectral Imagery, Torsten E. Howard
Theses and Dissertations
Modern warfare methods in the urban environment necessitates the use of multiple layers of sensors to manage the battle space. Hyperspectral imagers are one possible sensor modality to provide remotely sensed images that can be converted into Geographic Information Systems (GIS) layers. GIS layers abstract knowledge of roads, buildings, and scene content and contain shape files that outline and highlight scene features. Creating shape files is a labor-intensive and time-consuming process. The availability of shape files that reflect the current configuration of an area of interest significantly enhances Intelligence Preparation of the Battlespace (IPB). The solution presented in this thesis …
Statistical Methods For Image Registration And Denoising, Matthew D. Sambora
Statistical Methods For Image Registration And Denoising, Matthew D. Sambora
Theses and Dissertations
This dissertation describes research into image processing techniques that enhance military operational and support activities. The research extends existing work on image registration by introducing a novel method that exploits local correlations to improve the performance of projection-based image registration algorithms. The dissertation also extends the bounds on image registration performance for both projection-based and full-frame image registration algorithms and extends the Barankin bound from the one-dimensional case to the problem of two-dimensional image registration. It is demonstrated that in some instances, the Cramer-Rao lower bound is an overly-optimistic predictor of image registration performance and that under some conditions, the …
Image Super-Resolution Using Adaptive 2-D Gaussian Basis Function Interpolation, Terence D. Hunt
Image Super-Resolution Using Adaptive 2-D Gaussian Basis Function Interpolation, Terence D. Hunt
Theses and Dissertations
Digital image interpolation using Gaussian radial basis functions has been implemented by several investigators, and promising results have been obtained; however, determining the basis function variance has been problematic. Here, adaptive Gaussian basis functions fit the mean vector and covariance matrix of a non-radial Gaussian function to each pixel and its neighbors, which enables edges and other image characteristics to be more effectively represented. The interpolation is constrained to reproduce the original image mean gray level, and the mean basis function variance is determined using the expected image smoothness for the increased resolution. Test outputs from the resulting Adaptive Gaussian …
Redundant Wavelet-Based Image Restoration Using A Prior Information, Mary K. Marcum
Redundant Wavelet-Based Image Restoration Using A Prior Information, Mary K. Marcum
Theses and Dissertations
Reconnaissance missions and satellites collect hundreds of images loaded with valuable information to be utilized by the Air Force. Intelligence operations must analyze these images to extract the information needed to help commanders make important decisions. No matter how obtained, images of this type are often degraded by noise due to disruptions such as atmospheric disturbances, optical system variations, motion, and large distance from the sensor to the source. This noise must be removed effectively to improve the quality of these images and ensure that the information contained in them can be correctly extracted. The Air Force relies on the …
Human Visual System Enhancement Of Reconstructed Satellite Images, James E. Treleaven
Human Visual System Enhancement Of Reconstructed Satellite Images, James E. Treleaven
Theses and Dissertations
This research investigated the enhancement of satellite images. The goal was to develop and test a suite of image enhancement software routines to improve the quality of reconstructed images for the human visual system. The primary focus was to enhance satellite features. Enhancement was accomplished in both the spatial domain and the frequency domain. In the spatial domain, routines were developed to enhance image contrast and edges. In the frequency domain, a routine was developed using research into the human visual system. The transfer function of the human visual system was used to develop a filter for frequency domain enhancement. …
Objective Image Quality Metrics: Applications For Partially Compensated Images Of Space Objects, David J. Lee
Objective Image Quality Metrics: Applications For Partially Compensated Images Of Space Objects, David J. Lee
Theses and Dissertations
Digital image reconstruction tasks currently require human intervention for a subjective evaluation of image quality. A method for unsupervised measurement of digital image quality is desired. This research investigated various parameters metrics that can be automatically extracted from a digital image and tested how well they correlated with image quality. Specifically, images of orbiting satellites captured by a partially compensated adaptive optics telescope were dealt with. Two different types of quantities were investigated 1) Fourier spectral parameters, based on the spatial- frequency sensitivities of the HVS; and 2) Histogram shape parameters i.e image statistical moments giving quantitative insight into the …