Marcos J. Montes

Bio: Marcos J. Montes is an academic researcher from United States Naval Research Laboratory. The author has contributed to research in topics: Hyperspectral imaging & Supernova. The author has an hindex of 23, co-authored 87 publications receiving 2497 citations. Previous affiliations of Marcos J. Montes include Stanford University & United States Department of the Navy.

Radio emission from supernovae and gamma-ray bursters

Abstract: ▪ Abstract Study of radio supernovae over the past 20 years includes two dozen detected objects and more than 100 upper limits. From this work it is possible to identify classes of radio properties, demonstrate conformance to and deviations from existing models, estimate the density and structure of the circumstellar material and, by inference, the evolution of the presupernova stellar wind, and reveal the last stages of stellar evolution before explosion. It is also possible to detect ionized hydrogen along the line of sight, to demonstrate binary properties of the stellar system, and to show clumpiness of the circumstellar material. More speculatively, it may be possible to provide distance estimates to radio supernovae. Over the past four years the afterglow of gamma-ray bursters has occasionally been detected in the radio, as well in other wavelengths bands. In particular, the interesting and unusual gamma-ray burst GRB980425, thought to be related to SN1998bw, is a possible link between supernovae an...

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables

Abstract: A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (Rrs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured Rrs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.

Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space

Abstract: Existing atmospheric correction algorithms for multichannel remote sensing of ocean color from space were designed for retrieving water-leaving radiances in the visible over clear deep ocean areas and cannot easily be modified for retrievals over turbid coastal waters. We have developed an atmospheric correction algorithm for hyperspectral remote sensing of ocean color with the near-future Coastal Ocean Imaging Spectrometer. The algorithm uses lookup tables generated with a vector radiative transfer code. Aerosol parameters are determined by a spectrum-matching technique that uses channels located at wavelengths longer than 0.86 µm. The aerosol information is extracted back to the visible based on aerosol models during the retrieval of water-leaving radiances. Quite reasonable water-leaving radiances have been obtained when our algorithm was applied to process hyperspectral imaging data acquired with an airborne imaging spectrometer.

Large-aperture multiple quantum well modulating retroreflector for free-space optical data transfer on unmanned aerial vehicles

Abstract: We describe progress in the development of a multiple quan- tum well modulating retroreflector, including a description of recent dem- onstrations of an infrared data link between a small rotary-wing un- manned airborne vehicle and a ground-based laser interrogator using the device designed and fabricated at the Naval Research Laboratory (NRL). Modulating retroreflector systems couple an optical retroreflector, such as a corner cube, and an electro-optic shutter to allow two-way optical communications using a laser, telescope, and pointer-tracker on only one platform. The NRL modulating retroreflector uses a semiconductor-based multiple quantum well shutter capable of modula- tion rates greater than 10 Mbps, depending on link characteristics. The technology enables the use of near-infrared frequencies, which is well known to provide covert communications immune to frequency allocation problems. This specific device has the added advantage of being com- pact, lightweight, covert, and requires very low paper. Up to an order of magnitude in onboard power can be saved using a small array of these devices instead of the radio frequency equivalent. In the described dem- onstration, a Mbps optical link to an unmanned aerial vehicle in flight at a range of 100 to 200 feet is shown. Near real-time compressed video was also demonstrated at the Mbps level and is described. © 2001 Society of

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.

The Supernova Gamma-Ray Burst Connection

Abstract: Observations show that at least some gamma-ray bursts (GRBs) happen simultaneously with core-collapse supernovae (SNe), thus linking by a common thread nature's two grandest explosions. We review here the growing evidence for and theoretical implications of this association, and conclude that most long-duration soft-spectrum GRBs are accompanied by massive stellar explosions (GRB-SNe). The kinetic energy and luminosity of well-studied GRB-SNe appear to be greater than those of ordinary SNe, but evidence exists, even in a limited sample, for considerable diversity. The existing sample also suggests that most of the energy in the explosion is contained in nonrelativistic ejecta (producing the supernova) rather than in the relativistic jets responsible for making the burst and its afterglow. Neither all SNe, nor even all SNe of Type Ibc produce GRBs. The degree of differential rotation in the collapsing iron core of massive stars when they die may be what makes the difference.

Hyperspectral Subspace Identification

Abstract: Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

Theories of Gamma-Ray Bursts

Abstract: ▪ Abstract The gamma ray burst phenomenon is reviewed from a theoretical point of view, with emphasis on the fireball shock scenario of the prompt emission and the longer wavelength afterglow. Rece...