Author
Jessica Faust
Bio: Jessica Faust is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Imaging spectrometer & Airborne visible/infrared imaging spectrometer. The author has an hindex of 5, co-authored 8 publications receiving 1639 citations.
Papers
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TL;DR: The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) was the first imaging sensor to measure the solar reflected spectrum from 400 nm to 2500 nm at 10 nm intervals as mentioned in this paper.
1,729 citations
01 Jan 1996
TL;DR: In this article, an in-flight calibration experiment was performed for an over-flight on May 9, 1995 at Ivanpah Playa, California, where in-situ measurements were used to constrain a radiative transfer code and predict the total upwelling spectral radiance incident at AVIRIS.
Abstract: Calibrated spectra acquired remotely as images allow determination of surface and atmospheric properties based on absorption and scattering expressed in the spectra. AVIRIS measures spectra as images in the solar reflected portion of the electromagnetic spectrum. To use these spectra for scientific research and applications, the calibration of the spectra must be known at the time of measurement in flight. To validate the calibration of AVIRIS in flight, an in-flight calibration experiment was performed for an over-flight on May 9, 1995 at Ivanpah Playa, California. In-situ measurements of the atmosphere and surface at a calibration target were measured. These measurements were used to constrain a radiative transfer code and predict the total upwelling spectral radiance incident at AVIRIS. This prediction was compared to the radiance measured by AVIRIS for the calibration target. An agreement of 96.5% was determined. The in-flight signal-to-noise was determined and shown to have more than doubled over the previous year.
28 citations
19 Dec 1998
TL;DR: In this article, an inflight calibration experiment was conducted to validate the calibration of AVIRIS spectral images in the low pressure, low temperature operating environment of the ER-2.
Abstract: The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) measures spectral radiance in the solar reflected spectrum from 400 to 2500 nm. Spectra are measured through 224 spectral channels with nominally 10-nm sampling and 10-nm full width at half maximum (FWHM). From a NASA ER-2 aircraft flying at 20,000 m altitude, these spectra are acquired as images with an 11-km width by up to 800-km length. The spatial sampling is 17 m, and the instantaneous field of view (IFOV) 20 m. The objective of AVIRIS is to acquire calibrated spectra that are used to derive properties of the Earth's land, water, and atmosphere for scientific research and environmental applications. To achieve this objective, the AVIRIS spectra must be calibrated. The AVIRIS sensor is calibrated in the laboratory before and after each flight season, however, the spectra acquired by AVIRIS for science investigators are acquired in the Q-bay of the ER-2 at 20 km altitude. The objective of the AVIRIS inflight calibration experiment is to validate the calibration of AVIRIS spectral images in the low pressure, low temperature operating environment of the ER-2. Inflight calibration experiments have been orchestrated for AVIRIS in every year of flight operations.
24 citations
23 Jan 1995
TL;DR: In this paper, the authors proposed new techniques for the characterization of the geometric, spectral, temporal, and radiometric properties of the sensor, which can increase measurement accuracy and precision, minimize measurement time and expense, prototype new field and inflight calibration systems, resolve measurement ambiguities, and add new measurement dimensions.
Abstract: Recent laboratory calibrations of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) include new methods for the characterization of the geometric, spectral, temporal and radiometric properties of the sensor. New techniques are desired in order to: (1) increase measurement accuracy and precision, (2) minimize measurement time and expense, (3) prototype new field and inflight calibration systems, (4) resolve measurement ambiguities, and (5) add new measurement dimensions. One of the common features of these new methods is the use of the full data collection and processing power of the AVIRIS instrument and data facility. This allows the collection of large amounts of calibration data in a short period of time and is well suited to modular data analysis routines.
14 citations
23 Feb 2000
TL;DR: In this paper, the authors describe recent modifications to the instrument over winter maintenance cycles not covered elsewhere, and discuss the rationale leading up to the changes, implementation and results, as well as the results of the experiments.
Abstract: This paper describes recent modifications to the instrument over winter maintenance cycles not covered elsewhere, and discusses the rationale leading up to the changes, implementation and results.
7 citations
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TL;DR: A new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA), which competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
Abstract: Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
2,422 citations
TL;DR: This paper presents an overview of un Mixing methods from the time of Keshava and Mustard's unmixing tutorial to the present, including Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixed algorithms.
Abstract: Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.
2,373 citations
Posted Content•
TL;DR: An overview of unmixing methods from the time of Keshava and Mustard's tutorial as mentioned in this paper to the present can be found in Section 2.2.1].
Abstract: Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.
1,808 citations
TL;DR: The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) was the first imaging sensor to measure the solar reflected spectrum from 400 nm to 2500 nm at 10 nm intervals as mentioned in this paper.
1,729 citations
TL;DR: A seminal view on recent advances in techniques for hyperspectral image processing, focusing on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information.
1,481 citations