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Efficient Predictive Lossless Hyperspectral Image Compression Using Machine Learning, Zhuocheng Jiang
Efficient Predictive Lossless Hyperspectral Image Compression Using Machine Learning, Zhuocheng Jiang
Dissertations
Hyperspectral imaging technology has found many useful applications in various domains such as remote sensing. Data compression allows for efficient storage and transmission of massive hyperspectral image datasets. In this dissertation, we study efficient predictive coding schemes for lossless compression of hyperspectral images. We use machine learning techniques to improve the following two key components of the predictive coding process: (i) accurate pixel value prediction, and (ii) more efficient entropy coding of the prediction errors (residues). To this end, we propose an adaptive filtering framework based on concatenated neural networks, which are capable of extracting both spatial and spectral correlations …
Feature Extraction For Classification Of Auroral Images, Shwetha Herga
Feature Extraction For Classification Of Auroral Images, Shwetha Herga
Theses
Auroras are a dynamically evolving phenomenon. Different auroral forms are correlated with various physical processes in the magnetosphere and ionosphere system. Millions of auroral images are captured every year by the modern ground-based All-Sky Imager(ASI). In dealing with data from ASI, machine learning techniques play a critical scientific role, facilitating both efficient searches and statistical studies. In this work, we manually label night-side auroral images from various Time History of Events and Macroscale Interactions during Substorms (THEMIS) all-sky imager based on the sky conditions; the labels are clear sky with auroras, cloudy with the moon, cloudy, clear-sky with the moon, …