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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 …
Hyperspectral-Based Adaptive Matched Filter Detector Error As A Function Of Atmospheric Profile Estimation, Allan W. Yarbrough
Hyperspectral-Based Adaptive Matched Filter Detector Error As A Function Of Atmospheric Profile Estimation, Allan W. Yarbrough
Theses and Dissertations
Hyperspectral imagery is collected as radiance data. This data is a function of multiple variables: the radiation profile of the light source, the reflectance of the target, and the absorption and scattering profile of the medium through which the radiation travels as it reflects off the target and reaches the imager. Accurate target detection requires that the collected image matches as closely as possible the known "true" target in the classification database. Therefore, the effect of the radiation source and the atmosphere must be removed before detection is attempted. While the spectrum of solar light is relatively stable, the effect …
Fusion Schemes For Ensembles Of Hyperspectral Anomaly Detection Algorithms, Brooks R. Turnquist
Fusion Schemes For Ensembles Of Hyperspectral Anomaly Detection Algorithms, Brooks R. Turnquist
Theses and Dissertations
Hyperspectral imaging is playing an ever increasing role in our military's remote sensing operations. The exponential increase in collection operations generates more data than can be evaluated by analysts unassisted. Anomaly detectors attempt to reduce this load on analysts by identifying potential target pixels which appear anomalous when compared to what are determined to be background, or non-target, pixels. However, there is no one individual algorithm that is best suited for all situations and it can be difficult to choose the best algorithm for each individual task. Fusion techniques have been shown to reduce errors and increase generalization, eliminating the …