A review of the contributions of Alexander F.H. Goetz to imaging spectroscopy

All aspects of the science and engineering of imaging spectrometry have been advanced by the work of Dr. Alexander F.H. Goetz over the past 30 years. Dr. Goetz’s pioneering efforts were among the first to realize that it was feasible to obtain laboratory like spectra from space that would quantify earth materials based on biogeochemistry. He has made fundamental contributions to developing high spectral resolution field spectrometers and airborne imaging spectrometers, and to the image processing software and atmospheric correction software needed to analyze the data. These parallel developments in core technologies have made imaging spectroscopy available to a wide range of users of varying user expertise and disciplines, thus enabling the current state of rapid advances in the use of this data

A review of the contributions of Dr. Alexander F. H. Goetz to imaging spectroscopy

All aspects of the science and engineering of imaging spectrometry have been advanced by the work of Dr. Alexander F.H. Goetz over the past 30 years. Dr. Goetz¿s pioneering efforts were among the first to realize that it was feasible to obtain laboratory like spectra from space that would quantify earth materials based on biogeochemistry. He has made fundamental contributions to developing high spectral resolution field spectrometers and airborne imaging spectrometers, and to the image processing software and atmospheric correction software needed to analyze the data. These parallel developments in core technologies have made imaging spectroscopy available to a wide range of users of varying user expertise and disciplines, thus enabling the current state of rapid advances in the use of this dat

Proceedings of the Second Airborne Imaging Spectrometer Data Analysis Workshop

Topics addressed include: calibration, the atmosphere, data problems and techniques, geological research, and botanical and geobotanical research

Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop

This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5. The summaries are contained in Volumes 1, 2, and 3, respectively

Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop

The Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on April 8 to 10, 1985. It was attended by 92 people who heard reports on 30 investigations currently under way using AIS data that have been collected over the past two years. Written summaries of 27 of the presentations are in these Proceedings. Many of the results presented at the Workshop are preliminary because most investigators have been working with this fundamentally new type of data for only a relatively short time. Nevertheless, several conclusions can be drawn from the Workshop presentations concerning the value of imaging spectrometry to Earth remote sensing. First, work with AIS has shown that direct identification of minerals through high spectral resolution imaging is a reality for a wide range of materials and geological settings. Second, there are strong indications that high spectral resolution remote sensing will enhance the ability to map vegetation species. There are also good indications that imaging spectrometry will be useful for biochemical studies of vegetation. Finally, there are a number of new data analysis techniques under development which should lead to more efficient and complete information extraction from imaging spectrometer data. The results of the Workshop indicate that as experience is gained with this new class of data, and as new analysis methodologies are developed and applied, the value of imaging spectrometry should increase

HIRIS (High-Resolution Imaging Spectrometer: Science opportunities for the 1990s. Earth observing system. Volume 2C: Instrument panel report

The high-resolution imaging spectrometer (HIRIS) is an Earth Observing System (EOS) sensor developed for high spatial and spectral resolution. It can acquire more information in the 0.4 to 2.5 micrometer spectral region than any other sensor yet envisioned. Its capability for critical sampling at high spatial resolution makes it an ideal complement to the MODIS (moderate-resolution imaging spectrometer) and HMMR (high-resolution multifrequency microwave radiometer), lower resolution sensors designed for repetitive coverage. With HIRIS it is possible to observe transient processes in a multistage remote sensing strategy for Earth observations on a global scale. The objectives, science requirements, and current sensor design of the HIRIS are discussed along with the synergism of the sensor with other EOS instruments and data handling and processing requirements

Virtual Hyperspectral Images Using Symmetric Autoencoders

Spectral data acquired through remote sensing are invaluable for environmental and resource studies. However, these datasets are often marred by nuisance phenomena such as atmospheric interference and other complexities, which pose significant challenges for accurate analysis. We show that an autoencoder architecture, called symmetric autoencoder (SymAE), which leverages symmetry under reordering of the pixels, can learn to disentangle the influence of these nuisance from surface reflectance features on a pixel-by-pixel basis. The disentanglement provides an alternative to atmospheric correction, without relying on radiative transfer modelling, through a purely data-driven process. More importantly, SymAE can generate virtual hyperspectral images by manipulating the nuisance effects of each pixel. We demonstrate using AVIRIS instrument data that these virtual images are valuable for subsequent image analysis tasks. We also show SymAE's ability to extract intra-class invariant features, which is very useful in clustering and classification tasks, delivering state-of-the-art classification performance for a purely spectral method

Are circulating cytokines reliable biomarkers for amyotrophic lateral sclerosis?

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that has no effective treatment. The lack of any specific biomarker that can help in the diagnosis or prognosis of ALS has made the identification of biomarkers an urgent challenge. Multiple panels have shown alterations in levels of numerous cytokines in ALS, supporting the contribution of neuroinflammation to the progressive motor neuron loss. However, none of them is fully sensitive and specific enough to become a universal biomarker for ALS. This review gathers the numerous circulating cytokines that have been found dysregulated in both ALS animal models and patients. Particularly, it highlights the opposing results found in the literature to date, and points out another potential application of inflammatory cytokines as therapeutic targets

A probablistic framework for classification and fusion of remotely sensed hyperspectral data

Reliable and accurate material identification is a crucial component underlying higher-level autonomous tasks within the context of autonomous mining. Such tasks can include exploration, reconnaissance and guidance of machines (e.g. autonomous diggers and haul trucks) to mine sites. This thesis focuses on the problem of classification of materials (rocks and minerals) using high spatial and high spectral resolution (hyperspectral) imagery, collected remotely from mine faces in operational open pit mines. A new method is developed for the classification of hyperspectral data including field spectra and imagery using a probabilistic framework and Gaussian Process regression. The developed method uses, for the first time, the Observation Angle Dependent (OAD) covariance function to classify high-dimensional sets of data. The performance of the proposed method of classification is assessed and compared to standard methods used for the classification of hyperspectral data. This is done using a staged experimental framework. First, the proposed method is tested using high-resolution field spectrometer data acquired in the laboratory and in the field. Second, the method is extended to work on hyperspectral imagery acquired in the laboratory and its performance evaluated. Finally, the method is evaluated for imagery acquired from a mine face under natural illumination and the use of independent spectral libraries to classify imagery is explored. A probabilistic framework was selected because it best enables the integration of internal and external information from a variety of sensors. To demonstrate advantages of the proposed GP-OAD method over existing, deterministic methods, a new framework is proposed to fuse hyperspectral images using the classified probabilistic outputs from several different images acquired of the same mine face. This method maximises the amount of information but reduces the amount of data by condensing all available information into a single map. Thus, the proposed fusion framework removes the need to manually select a single classification among many individual classifications of a mine face as the `best' one and increases the classification performance by combining more information. The methods proposed in this thesis are steps forward towards an automated mine face inspection system that can be used within the existing autonomous mining framework to improve productivity and efficiency. Last but not least the proposed methods will also contribute to increased mine safety