Charles M. Sarture

Bio: Charles M. Sarture 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 11, co-authored 19 publications receiving 2050 citations.

A method for quantitative mapping of thick oil spills using imaging spectroscopy

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Portable Remote Imaging Spectrometer coastal ocean sensor: design, characteristics, and first flight results

Abstract: The design, characteristics, and first test flight results are described of the Portable Remote Imaging Spectrometer, an airborne sensor specifically designed to address the challenges of coastal ocean remote sensing. The sensor incorporates several technologies that are demonstrated for the first time, to the best of our knowledge, in a working system in order to achieve a high performance level in terms of uniformity, signal-to-noise ratio, low polarization sensitivity, low stray light, and high spatial resolution. The instrument covers the 350–1050 nm spectral range with a 2.83 nm sampling per pixel, and a 0.88 mrad instantaneous field of view, with 608 cross-track pixels in a pushbroom configuration. Two additional infrared channels (1240 and 1610 nm) are measured by a spot radiometer housed in the same head. The spectrometer design is based on an optically fast (F/1.8) Dyson design form coupled to a wide angle two-mirror telescope in a configuration that minimizes polarization sensitivity without the use of a depolarizer. A grating with minimum polarization sensitivity and broadband efficiency was fabricated as well as a slit assembly with black (etched) silicon surface to minimize backscatter. First flight results over calibration sites as well as Monterey Bay in California have demonstrated good agreement between in situ and remotely sensed data, confirming the potential value of the sensor to the coastal ocean science community.

The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR): instrument description and first results

Abstract: An Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) instrument has been developed to assist in validation of the Earth Observing System (EOS) MISR experiment. Unlike the EOS MISR, which contains nine individual cameras pointed at discrete look angles, AirMISR utilizes a single camera in a pivoting gimbal mount. The AirMISR camera has been fabricated from MISR brassboard and engineering model components and, thus, has similar radiometric and spectral response as the MISR cameras. This paper provides a description of the AirMISR instrument and summarizes the results of engineering flights conducted during 1997.

Vertex component analysis: a fast algorithm to unmix hyperspectral data

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.

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

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.

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

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.