Charles Bachmann, Andrei Abelev, Marcos Montes, William Philpot, Deric Gray, Katarina Doctor, Robert Fusina, Gordon Mattis, Wei Chen, Scott Noble, Craig Coburn, Tom Corl, Lawrence Slomer, C. Reid Nichols, Elena van Roggen, Roy Hughes, Stephen Carr, Sergey Kharabash, Andrew Brady, Michael Vermillion
This paper describes a portable hyperspectral goniometer system for measurement of hemispherical conical reflectance factor (HCRF) data for terrestrial applications, especially in the coastal zone. This system, the Goniometer for Portable Hyperspectral Earth Reflectance (GOPHER), consists of a computer-controlled Spectra Vista Corporation HR-1024 full-range spectrometer mounted on a rotating arc and track assembly, allowing complete coverage in zenith and azimuth of a full hemisphere for recording HCRF. The control software allows customized scan patterns to be quickly modified in the field, providing for flexibility in recording HCRF and the opposition effect with varying grid sizes and scan ranges in both azimuth and zenith directions. The spectrometer track can be raised and lowered on a mast to accommodate variations in terrain and land cover. To minimize the effect of variations in illumination during GOPHER scan cycles, a dual-spectrometer approach has been adapted to link records of irradiance recorded by a second spectrometer during the GOPHER HCRF scan cycle. Examples of field data illustrate the utility of the instrument for coastal studies.
The Global Optimal Solution (GOS) provides surface velocities from Advanced Very High Resolution Radiometer
(AVHRR) remote image sequences by using bilinear interpolation algorithms. A highly accurate velocity field can be
estimated by GOS with infrared image sequence, but the field has only first order continuity. This paper deals with the
use of GOS, but with higher order continuity and smoothed cutouts around coastland edges to estimate surface velocities.
Since an actual coastal ocean has a complex, irregular coastland, and some ocean studies need vorticity and divergence
analysis which must be extracted from the velocity field, the development of generic GOS algorithms with higher order
continuity and smoothed cutouts around these edges is very important.
In this paper, the GOS bilinear polynomials, formerly applied only to square tiles with first order continuity, are replaced
by surface B-Splines functions. The new GOS algorithms can be applied to AVHRR images containing complicated
coastal land boundaries - or even clouds - to yield smooth velocity fields next to land, and higher order continuity
velocity field can be obtained. The main advantages of the new GOS are that the highly accurate solution is global
optimized, linear, and high order smoothed. The high order GOS velocity fields with those from the numerical model
and from the first order GOS technique are compared. Results of applying these methods to two real image sequences are
presented. It is demonstrated in this paper that this high order GOS technique to two sequences of NOAA satellite
AVHRR images taken in the New York Bight to calculate a velocity field adjacent to the land. I found that all results of
the angular and magnitude errors of the velocity by 1st and 3rd order GOS are quite close for both numerical model data
and AVHRR image sequences, but velocity field estimated by 3rd order GOS is global smoothed.
This paper demonstrates the characterization of the water properties, bathymetry, and bottom type of the Indian River Lagoon (IRL) on the eastern coast of Florida using hyperspectral imagery. Images of this region were collected from an aircraft in July 2004 using the Portable Hyperspectral Imager for Low Light Spectroscopy (PHILLS). PHILLS is a Visible Near InfraRed (VNIR) spectrometer that was operated at an altitude of 3000 m providing 4 m resolution with 128 bands from 400 to 1000 nm. The IRL is a well studied water body that receives fresh water drainage from the Florida Everglades and also tidal driven flushing of ocean water through several outlets in the barrier islands. Ground truth measurements of the bathymetry of IRL were acquired from recent sonar and LIDAR bathymetry maps as well as water quality studies concurrent to the hyperspectral data collections. From these measurements, bottom types are known to include sea grass, various algae, and a gray mud with water depths less than 6 m over most of the lagoon. Suspended sediments are significant (~35 mg/m3) with chlorophyll levels less than 10 mg/m3 while the absorption due to Colored Dissolved Organic Matter (CDOM) is less than 1 m-1 at 440 nm. Hyperspectral data were atmospherically corrected using an NRL software package called Tafkaa and then subjected to a Look-Up Table (LUT) approach which matches hyperspectral data to calculated spectra with known values for bathymetry, suspended sediments, chlorophyll, CDOM, and bottom type.
The Naval Research Laboratory and the Boeing Company have teamed to fly the NRL ocean Portable Hyperspectral Imager for Low Light Spectroscopy (ocean PHILLS) on board the International Space Station (ISS). This joint program is named the Hyperspectral Sensor for Global Environmental Imaging and Analysis (HyGEIA). Hyperspectral images spanning the wavelength range 400 to 1000 nm will be collected at a ground sample distance of 25 m, with 10 nm spectral binning, and 200 to 1 signal to noise over the visible wavelengths for a 5% albedo scene. These images will be used to characterize the coastal ocean and littoral zone, crops, and forest areas. The PHILLS will also image over the same wavelength range at 130 m GSD to produce similar environmental products over a larger ground area. This paper will describe the modification of PHILLS required for use on the ISS, the modeled on orbit performance, and the planned on orbit configuration.
KEYWORDS: Algorithm development, Wavelets, Detection and tracking algorithms, Target detection, Target recognition, Sensors, Signal to noise ratio, RGB color model, Error analysis, Analytical research
The Optical Real-time Adaptive Spectral Identification System (ORASIS) continues to be developed by the Naval Research Laboratory. Recently, new methods to compress hyperspectral data using ORASIS in combination with wavelets have been demonstrated. Variations on this approach continue to be pursued and will be discussed. In addition, a new method to perform target identification based on Spectral Angle Mapper (SAM) has been demonstrated. When projected into a reduced dimension space, angles between spectra are not conserved. Proper determination of the dimensions and coordinates of the subspace can result in maximizing the angles between a spectrum of interest and the general background. This approach can be used for target identification where the "target" can be a rare spectrum or something found in the general background. Recent improvements to these methods, as well as new applications of the software, are discussed.
A scheme for lossy hyperspectral data cube compression, using a linear mixing model approach and wavelet transform, is presented. The data is first compressed in the spectral dimension by using the linear mixing model approximation to reduce the number of dimensions needed to represent the data. The reduced data is then compressed along the spatial dimensions using a wavelet transform. Five hyperspectral data cubes have been tested using the algorithm. Compression ratios of up to 1000:1 are achieved with peak signal-to-noise (PSNR) ratios of over 40 dB. For all test cases, we were able to achieve ratios of over 200:1 with PSNR exceeding 46 dB. The ultra-high compression ratio with low distortion is an improvement over other results reported in the literature. In addition, the reconstructed spectra from the highly compressed file are shown to preserve the overall shape of the original spectra. However, in some cases the curves are slightly offset in some spectral regions from the original.
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