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Full-Text Articles in Engineering
Manifold Learning Based Spectral Unmixing Of Hyperspectral Remote Sensing Data, Jun-Hwa Chi
Manifold Learning Based Spectral Unmixing Of Hyperspectral Remote Sensing Data, Jun-Hwa Chi
Open Access Dissertations
Nonlinear mixing effects inherent in hyperspectral data are not properly represented in linear spectral unmixing models. Although direct nonlinear unmixing models provide capability to capture nonlinear phenomena, they are difficult to formulate and the results are not always generalizable. Manifold learning based spectral unmixing accommodates nonlinearity in the data in the feature extraction stage followed by linear mixing, thereby incorporating some characteristics of nonlinearity while retaining advantages of linear unmixing approaches. Since endmember selection is critical to successful spectral unmixing, it is important to select proper endmembers from the manifold space. However, excessive computational burden hinders development of manifolds for …
Optical Sensors For Mapping Temperature And Winds In The Thermosphere From A Cubesat Platform, Stephanie Whalen Sullivan
Optical Sensors For Mapping Temperature And Winds In The Thermosphere From A Cubesat Platform, Stephanie Whalen Sullivan
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
In the same way that mariners desire to know the weather their ships are about to encounter, satellite owners would like to know the conditions along their satellites' orbits. Accurate forecasts would allow operators to secure sensitive components prior to passing through a storm to reduce the risk of damage. Large solar arrays, which can act like sails, can be re-oriented to prevent the satellite from being moved out of its desired orbit. While terrestrial weather forecasters have thousands of sensors with continuous data streams available to generate weather models, very few sensors exist for space weather. Cost of sensing …