Hyper-Spectral Image Generation from Frequency Spectrums

Abstract

My thesis primarily focuses on hyper-spectral image generation from frequency spectrums for downstream computer vision tasks. Hyper-spectral images are images with more than three channels commonly created by special hyper-spectral cameras or from frequency spectrums of various sensing applications such as radargrams or distributed acoustic sensing (DAS) systems. The range of frequencies considered in a frequency spectrum is typically too large to map one frequency to one image channel, i.e. we generally consider a frequency spectrum of 2500 Hz. Frequencies need to be binned together in frequency bands where each band forms one image channel. Usually, frequency bands are created either by expert knowledge or trial-and-error. I research how filters can be trained to automatically select frequencies and bin them into frequency bands. My aim is to represent a variety of signal information and decrease noise. Signal representation is optimised for object detection on time-sequenced images with a set number of image channels. The object detection task consists of localising and classifying events in the generated hyper-spectral images. Events are typically types of intrusions, structural changes, or defined actions and structures, e.g. someone climbing a fence. Events and noise often share at least some frequencies and vary between application types.