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Early Forest Fire Heat Plume Detection Using Neural Network Classification Of Spectral Differences Between Long-Wave And Mid-Wave Infrared Regions, Raul-Alexander Aldama
Early Forest Fire Heat Plume Detection Using Neural Network Classification Of Spectral Differences Between Long-Wave And Mid-Wave Infrared Regions, Raul-Alexander Aldama
Master's Theses
It is difficult to capture the early signs of a forest fire at night using current visible-spectrum sensor technology. Infrared (IR) light sensors, on the other hand, can detect heat plumes expelled at the initial stages of a forest fire around the clock. Long-wave IR (LWIR) is commonly referred to as the “thermal infrared” region where thermal emissions are captured without the need of, or reflections from, external radiation sources. Mid‑wave IR (MWIR) bands lie between the “thermal infrared” and “reflected infrared” (i.e. short-wave IR) regions. Both LWIR and MWIR spectral regions are able to detect thermal radiation; however, they …
Applications Of Artificial Neural Networks To Synthetic Aperture Radar For Feature Extraction In Noisy Environments, David James Roberts
Applications Of Artificial Neural Networks To Synthetic Aperture Radar For Feature Extraction In Noisy Environments, David James Roberts
Master's Theses
It is often that images generated from Synthetic Aperture Radar (SAR) are noisy, distorted, or incomplete pictures of a target or target region. As the goal for most SAR research pertains to automatic target recognition (ATR), extensive filtering and image processing is required in order to extract the features necessary to carry out ATR. This thesis investigates the use of Artificial Neural Networks (ANNs) in order to improve upon the feature extraction process by laying the foundation for ANN SAR ATR algorithms and programs. The first technique investigated is that of an ANN edge detector designed to be invariant to …