A Contribution to Active Infrared Laser Spectroscopy for Remote Substance Detection

In this work we present a stand-off, long wave infrared (LWIR) spectroscopy system for remote detection of hazardous substances. The principle is based upon wavelength selective illumination using External Cavity Quantum Cascade Lasers, that are tunable in the LWIR wavelength range from 7.5 - 10 µm, in which most chemical substances exhibit a highly characteristic absorption behaviour

Detection of Marine Plastic Debris in the North Pacific Ocean using Optical Satellite Imagery

Plastic pollution is ubiquitous across marine environments, yet detection of anthropogenic debris in the global oceans is in its infancy. Here, we exploit high-resolution multispectral satellite imagery over the North Pacific Ocean and information from GPS-tracked floating plastic conglomerates to explore the potential for detecting marine plastic debris via spaceborne remote sensing platforms. Through an innovative method of estimating material abundance in mixed pixels, combined with an inverse spectral unmixing calculation, a spectral signature of aggregated plastic litter was derived from an 8-band WorldView-2 image. By leveraging the spectral characteristics of marine plastic debris in a real environment, plastic detectability was demonstrated and evaluated utilising a Spectral Angle Mapper (SAM) classification, Mixture Tuned Matched Filtering (MTMF), the Reed-Xiaoli Detector (RXD) algorithm, and spectral indices in a three-variable feature space. Results indicate that floating aggregations are detectable on sub-pixel scales, but as reliable ground truth information was restricted to a single confirmed target, detections were only validated by means of their respective spectral responses. Effects of atmospheric correction algorithms were evaluated using ACOLITE, ACOMP, and FLAASH, in which derived unbiased percentage differences ranged from 1% to 81% following a pairwise comparison. Building first steps towards an integrated marine monitoring system, the strengths and limitations of current remote sensing technology are identified and adopted to make suggestions for future improvements

Characterization of the spectral distribution of hyperspectral imagery for improved exploitation

Widely used methods of target, anomaly, and change detection when applied to spectral imagery provide less than desirable results due to the complex nature of the data. In the case of hyperspectral data, dimension reduction techniques are employed to reduce the amount of data used in the detection algorithms in order to produce better results and/or decreased computation time. This essentially ignores a significant amount of the data collected in k unique spectral bands. Methods presented in this work explore using the distribution of the collected data in the full k dimensions in order to identify regions of interest contained in spatial tiles of the scene. Here, interest is defined as small and large scale manmade activity. The algorithms developed in this research are primarily data driven with a limited number of assumptions. These algorithms will individually be applied to spatial subsets or tiles of the full scene to indicate the amount of interest contained. Each tile is put through a series of tests using the algorithms based on the full distribution of the data in the hyperspace. The scores from each test will be combined in such a way that each tile is labeled as either interesting or not interesting. This provides a cueing mechanism for image analysts to visually inspect locations within a hyperspectral scene with a high likelihood of containing manmade activity