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Full-Text Articles in Physics
Fast And Effective Techniques For Lwir Radiative Transfer Modeling: A Dimension-Reduction Approach, Nicholas M. Westing [*], Brett J. Borghetti, Kevin C. Gross
Fast And Effective Techniques For Lwir Radiative Transfer Modeling: A Dimension-Reduction Approach, Nicholas M. Westing [*], Brett J. Borghetti, Kevin C. Gross
Faculty Publications
The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. These detailed measurements allow for material classification, with many recent advancements from the fields of machine learning and deep learning. In many scenarios, the hyperspectral image must first be corrected or compensated for atmospheric effects. Radiative Transfer (RT) computations can provide look up tables (LUTs) to support these corrections. This research investigates a dimension-reduction approach using machine learning methods to create an effective sensor-specific long-wave infrared (LWIR) RT model.
Anomaly Detection In Hyperspectral Imagery: Comparison Of Methods Using Diurnal And Seasonal Data, Patrick C. Hytla, Russell C. Hardie, Michael T. Eismann, Joseph Meola
Anomaly Detection In Hyperspectral Imagery: Comparison Of Methods Using Diurnal And Seasonal Data, Patrick C. Hytla, Russell C. Hardie, Michael T. Eismann, Joseph Meola
Russell C. Hardie
The use of hyperspectral imaging is a fast growing field with many applications in the civilian, commercial and military sectors. Hyperspectral images are typically composed of many spectral bands in the visible and infrared regions of the electromagnetic spectrum and have the potential to deliver a great deal of information about a remotely sensed scene. One area of interest regarding hyperspectral images is anomaly detection, or the ability to find spectral outliers within a complex background in a scene with no a priori information about the scene or its specific contents. Anomaly detectors typically operate by creating a statistical background …
Anomaly Detection In Hyperspectral Imagery: Comparison Of Methods Using Diurnal And Seasonal Data, Patrick C. Hytla, Russell C. Hardie, Michael T. Eismann, Joseph Meola
Anomaly Detection In Hyperspectral Imagery: Comparison Of Methods Using Diurnal And Seasonal Data, Patrick C. Hytla, Russell C. Hardie, Michael T. Eismann, Joseph Meola
Electrical and Computer Engineering Faculty Publications
The use of hyperspectral imaging is a fast growing field with many applications in the civilian, commercial and military sectors. Hyperspectral images are typically composed of many spectral bands in the visible and infrared regions of the electromagnetic spectrum and have the potential to deliver a great deal of information about a remotely sensed scene. One area of interest regarding hyperspectral images is anomaly detection, or the ability to find spectral outliers within a complex background in a scene with no a priori information about the scene or its specific contents. Anomaly detectors typically operate by creating a statistical background …