In this work, we present a retrieval framework to invert aerosol microphysical parameters using ground-based hyper-angular polarimetry of the full-sky dome. Measurements are acquired with Mantis, a custom polarimeter built for the NRL by Polaris Sensor Technologies. We compare and contrast our results with Tafkaa, an at-altitude method of atmospheric correction currently used by the NRL with data collected at the Army Corps of Engineers Field Research Facility. We discuss and analyze the technique in this complex near-coastal environment and compare the derivation of remote sensing reflectance from both techniques with that measured in-situ.
The study of aquatic ecosystems is an important research area addressing diverse problems such as carbon sequestration in coastal margins and wetlands, kelp and seagrass studies, coral reefs, harmful algal blooms and hypoxia, and carbon cycling in this dynamic environment. The application of an imaging spectrometer to aquatic ecosystem study is particularly challenging due to low water-leaving radiance levels adjacent to the shore region with its higher values. The Committee on Earth Observation Satellites (CEOS) has established more stringent performance standards for the visible/near infrared wavelengths than are typically available in imaging spectrometer designs. We have recently developed a compact form imaging spectrometer, the Chrisp Compact VNIR/SWIR Imaging Spectrometer (CCVIS), that facilitates their modular usage with a wide field telescope without sacrificing performance. The CCVIS design and the operational concept have predicted performance that approaches the CEOS standards. The envisioned satellite implementation requires a pitchback maneuver where the imaging of the slit projected onto the surface is slowly scanned while recording focal plane array readouts at a higher rate thereby avoiding saturation over the land surface while obtaining a high signal-to-noise ratio over the water. The effective frame rate is determined by the time it takes to scan the projected slit one ground sample distance (GSD). This approach has the added benefit of measuring a range of angles during a single GSD acquisition, providing insight into the bidirectional reflectance distribution function (BRDF).
Here we report progress in the fabrication, calibration, and testing of a compact spectral imaging camera. The camera uses a micro-array of Fabry-Perot etalons bonded directly to a broadband focal plane array sensor. The array of etalons adds negligible size and weight to the system compared to a panchromatic imager. Other recent demonstrations of compact spectral imagers in the visible and near infrared (VNIR) have commonly used arrays of etalons in a single order, thereby reducing the system bandwidth and sensitivity to achieve the required spectral resolution. Here, we demonstrate a camera utilizing multiple etalon orders in a spectral multiplexing scheme known as Multiple Order Staircase Etalon Spectrometry (MOSES). An important characteristic of the MOSES system is that there is a relaxed tradeoff between spectral resolution and sensitivity (or etalon throughput). Unlike single-order etalon techniques, MOSES allows for the reconstruction of the spectrum to the bandwidth limit of the detector and reflecting layers. This is important in coastal environmental sensing, where IR spectral features may be desired at the same time as UV light transmitted through shallow water layers. This VNIR system demonstration indicates the feasibility of MOSES devices in other wavebands.
At the Naval Research Laboratory Optical Sciences and Remote Sensing Divisions, a compact hyperspectral imaging sensor has been in development using a method of multi-order spectroscopy using Fabry-Perot etalon arrays. This has allowed for the first broadband, ultra-compact spectrometer. A prototype hyperspectral imaging system is now in development for use with an unmanned aerial vehicle. This system will be a “push-broom system” that scans ground data line by line in a row of pixels forming the hyperspectral datacube and will be georeferenced onto a Digital Surface Model of the ground with location (latitude, longitude, altitude) and attitude (azimuth, pitch, roll) GPS-INS 6-degree of freedom parameters. These parameters will be collected through the use of a high-end modern smartphone with its GPS, accelerometer, gyroscope, barometric pressure, and digital compass sensors. In this paper, we discuss the various sensors and systems being utilized with a smartphone for use with the hyperspectral imaging sensor in development.
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.
Spectral variability in the visible, near-infrared and shortwave directional reflectance factor of beach sands and freshwater sheet flow is examined using principal component and correlation matrix analysis of in situ measurements. In previous work we concluded that the hyperspectral bidirectional reflectance distribution function (BRDF) of beach sands in the absence of sheet flow exhibit weak spectral variability, the majority of which can be described with three broad spectral bands with wavelength ranges of 350-450 nm, 700-1350 nm, and 1450-2400 nm.1 Observing sheet flow on sand we find that a thin layer of water enhances reflectance in the specular direction at all wavelengths and that spectral variability may be described using four spectral band regions of 350-450 nm, 500-950 nm, 950-1350 nm, and 1450-2400 nm. Spectral variations are more evident in sand surfaces of greater visual roughness than in smooth surfaces, regardless of sheet flow.
In past work, we have shown that density effects in hyperspectral bi-directional reflectance function (BRDF) data are consistent in laboratory goniometer data, field goniometer measurements with the NRL Goniometer for Portable Hyperspectral Earth Reflectance (GOPHER), and airborne CASI-1500 hyperspectral imagery. Density effects in granular materials have been described in radiative transfer models and are known, for example, to influence both the overall level of reflectance as well as the size of specific characteristics such as the width of the opposition effect in the BRDF. However, in mineralogically complex sands, such as coastal sands, the relative change in reflectance with density depends on the composite nature of the sand. This paper examines the use of laboratory and field hyperspectral goniometer data and their utility for retrieving sand density from airborne hyperspectral imagery. We focus on limitations of current models to describe density effects in BRDF data acquired in the field, laboratory setting, and from airborne systems.
Charles Bachmann, Deric Gray, Andrei Abelev, William Philpot, Marcos Montes, Robert Fusina, Joseph Musser, Rong-Rong Li, Michael Vermillion, Geoffrey Smith, Daniel Korwan, Charlotte Snow, W. David Miller, Joan Gardner, Mark Sletten, Georgi Georgiev, Barry Truitt, Marcus Killmon, Jon Sellars, Jason Woolard, Christopher Parrish, Art Schwarzscild
In June 2011, a multi-sensor airborne remote sensing campaign was flown at the Virginia Coast Reserve Long Term
Ecological Research site with coordinated ground and water calibration and validation (cal/val) measurements.
Remote sensing imagery acquired during the ten day exercise included hyperspectral imagery (CASI-1500),
topographic LiDAR, and thermal infra-red imagery, all simultaneously from the same aircraft. Airborne synthetic
aperture radar (SAR) data acquisition for a smaller subset of sites occurred in September 2011 (VCR'11). Focus
areas for VCR'11 were properties of beaches and tidal flats and barrier island vegetation and, in the water column,
shallow water bathymetry. On land, cal/val emphasized tidal flat and beach grain size distributions, density,
moisture content, and other geotechnical properties such as shear and bearing strength (dynamic deflection
modulus), which were related to hyperspectral BRDF measurements taken with the new NRL Goniometer for
Outdoor Portable Hyperspectral Earth Reflectance (GOPHER). This builds on our earlier work at this site in 2007
related to beach properties and shallow water bathymetry. A priority for VCR'11 was to collect and model
relationships between hyperspectral imagery, acquired from the aircraft at a variety of different phase angles, and
geotechnical properties of beaches and tidal flats. One aspect of this effort was a demonstration that sand density
differences are observable and consistent in reflectance spectra from GOPHER data, in CASI hyperspectral imagery,
as well as in hyperspectral goniometer measurements conducted in our laboratory after VCR'11.
Hyperion is a hyperspectral sensor on board NASA's EO-1 satellite with a spatial resolution of approximately 30 m and a swath width of about 7 km. It was originally designed for land applications, but its unique spectral configuration (430 nm - 2400 nm with a ~10 nm spectral resolution) and high spatial resolution make it attractive for studying complex coastal ecosystems, which require such a sensor for accurate retrieval of environmental properties. In this paper, Hyperion data over an area of the Florida Keys is used to develop and test algorithms for atmospheric correction and for retrieval of subsurface properties. Remote-sensing reflectance derived from Hyperion data is compared with those from in situ measurements. Furthermore, water's absorption coefficients and bathymetry derived from Hyperion imagery are compared with sample measurements and LIDAR survey, respectively. For a depth range of ~ 1 - 25 m, the Hyperion bathymetry match LIDAR data very well (~11% average error); while the absorption coefficients differ by ~16.5% (in a range of 0.04 - 0.7 m-1 for wavelengths of 410, 440, 490, 510, and 530 nm) on average. More importantly, in this top-to-bottom processing of Hyperion imagery, there is no use of any a priori or ground truth information. The results demonstrate the usefulness of such space-borne hyperspectral data and the techniques developed for effective and repetitive observation of complex coastal regions.
In this paper, we investigate the use of nonlinear structure to derive the physical characteristics of coastal data. In particular, we show how the physics of shallow water coastal regions lead to well defined nonlinear structures (manifolds) in the corresponding hyperspectral data. The exact form of this structure is determined by both the Inherent Optical Properties of the water column as well as the boundary conditions (bottom reflectance, depth). This structure is then used to develop efficient algorithms for searching large 'lookup tables' of precalculated spectra with known physical characteristics, which are used for estimating the various physical parameters (bathymetry, bottom type, etc.) of the scene.
We assess our methods with data collected by the NRL PHILLS sensor at the Indian River Lagoon (IRL) in Florida. The IRL is a well-studied and characterized body of water that contains a number of different water and bottom types at various shallow (generally less than 8 meters, except in the shipping channel where depths can be as much as 18 m) depths. We show in particular that the search algorithm is able to produce valid results in a short amount of time, and compare our results with an IRL LIDAR bathymetry survey from early 2004.
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.
A useful technique in hyperspectral data analysis is dimensionality reduction, which replaces the original high dimensional data with low dimensional representations. Usually this is done with linear techniques such as linear mixing or principal components (PCA). While often useful, there is no a priori reason for believing that the data is actually linear.
Lately there has been renewed interest in modeling high dimensional data using nonlinear techniques such as manifold learning (ML). In ML, the data is assumed to lie on a low dimensional, possibly curved surface (or manifold). The goal is to discover this manifold and therefore find the best low dimensional representation of the data.
Recently, researchers at the Naval Research Lab have begun to model hyperspectral data using ML. We continue this work by applying ML techniques to hyperspectral ocean water data. We focus on water since there are underlying physical reasons for believing that the data lies on a certain type of nonlinear manifold. In particular, ocean data is influenced by three factors: the water parameters, the bottom type, and the depth. For fixed water and bottom types, the spectra that arise by varying the depth will lie on a nonlinear, one dimensional manifold (i.e. a curve). Generally, water scenes will contain a number of different water and bottom types, each combination of which leads to a distinct curve. In this way, the scene may be modeled as a union of one dimensional curves. In this paper, we investigate the use of manifold learning techniques to separate the various curves, thus partitioning the scene into homogeneous areas. We also discuss ways in which these techniques may be able to derive various scene characteristics such as bathymetry.
We previously developed an algorithm named Tafkaa for remote sensing of ocean color from aircraft and satellite platforms. The algorithm allows quick atmospheric correction of hyperspectral data using lookup tables generated with a modified version of Ahmad & Fraser's vector radiative transfer code. During the past few years we have extended the capabilities of the code. Current modifications include the ability to account for varying solar geometry (important for very long scenes) and view geometries (important for wide fields of view). Additionally, versions of Tafkaa have been made for a variety of multi-spectral sensors, including SeaWiFS and MODIS. Here we present sample results of atmospheric corrections of data from several platforms.
We previously developed an algorithm for remote sensing of ocean color from space that allows quick atmospheric correction of hyperspectral data using lookup tables generated with a modified version of Ahmad & Fraser's vector radiative transfer code. During the past year we extended our radiative transfer calculations, allowing us to generate tables for several airborne altitudes. We also modified our lookup-table software to interpolate to sensor altitudes between those specified in the new tables. Here, we present results of atmospheric corrections using the new tables and software on hyperspectral imagery collected with NRL's recent PHILLS instrument and past AVIRIS flights.
Existing atmospheric correction algorithms for multi-channel remote sensing of ocean color from space were designed for retrieving water leaving radiances in the visible over clear deep ocean areas. The information about atmospheric aerosols is derived from channels between 0.66 and 0.87 micrometer, where the water leaving radiances are close to zero. The derived aerosol information is extrapolated back to the visible when retrieving water leaving radiances from remotely sensed data. For the turbid coastal environment, the water leaving radiances from the 0.66-micrometer channel may not be close to zero because of back scattering by suspended materials in the water. This channel may not be useful for deriving information on atmospheric aerosols. As a result, the algorithms developed for applications to clear ocean waters cannot be easily modified to retrieve water leaving radiances from remotely sensed data measured over the coastal environments. We have developed an atmospheric correction algorithm for hyperspectral remote sensing of ocean color with the near-future Coastal Ocean Imaging Spectrometer (COIS). The algorithm uses lookup tables generated with a vector radiative transfer code. Aerosol parameters are determined by a spectrum-matching technique utilizing channels located at wavelengths longer than 0.86 micrometer. The aerosol information is extracted back to the visible based on aerosol models during our retrieval of water leaving radiances. Quite reasonable results have been obtained when applying our algorithm to process hyperspectral imaging data acquired with an airborne imaging spectrometer.
In this paper, we describe progress in the development of the NRL Multiple Quantum Well modulating retro-reflector including a description of recent demonstrations of an infrared data link between a small rotary-wing unmanned airborne vehicle and a ground based laser interrogator using the NRL multiple quantum well modulating retro-reflector. Modulating retro-reflector systems couple an optical retro- reflector, 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 retro-reflector uses a semiconductor based multiple quantum well shutter capable of modulation rates up to 10 Mbps, depending on link characteristics. The technology enable the use of near-infrared frequencies, which is well known to provide covert communications immune to frequency allocation problems. The multiple quantum well modulating retro-reflector has the added advantage of being compact, lightweight, covert, and requires very low power. 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 demonstration, a Mbps optical link to an unmanned aerial vehicle in flight at a range of 100-200 feet is shown. Near real-time compressed video is also demonstrated at the Mbps level.
We present an overview of the Naval EarthMap Observer (NEMO) spacecraft and then focus on the processing of NEMO data both on-board the spacecraft and on the ground. The NEMO spacecraft provides for Joint Naval needs and demonstrates the use of hyperspectral imagery for the characterization of the littoral environment and for littoral ocean model development. NEMO is being funded jointly by the U.S. government and commercial partners. The Coastal Ocean Imaging Spectrometer (COIS) is the primary instrument on the NEMO and covers the spectral range from 400 to 2500 nm at 10-nm resolution with either 30 or 60 m work GSD. The hyperspectral data is processed on-board the NEMO using NRL's Optical Real-time Automated Spectral Identification System (ORASIS) algorithm that provides for real time analysis, feature extraction and greater than 10:1 data compression. The high compression factor allows for ground coverage of greater than 106 km2/day. Calibration of the sensor is done with a combination of moon imaging, using an onboard light source and vicarious calibration using a number of earth sites being monitored for that purpose. The data will be atmospherically corrected using ATREM. Algorithms will also be available to determine water clarity, bathymetry and bottom type.
Multi-channel remote sensing of ocean color from space has a rich history -- from the past CZCS, to the present SeaWiFS, and to the near-future MODIS. The atmospheric correction algorithms for processing remotely sensed data from these sensors were mainly developed by Howard Gordon at University of Miami. The algorithms were primarily designed for retrieving water leaving radiances in the visible spectral region over clear deep ocean areas. The information about atmospheric aerosols is derived from channels between 0.66 and 0.87 micrometer, where the water leaving radiances are close to zero. The derived aerosol information is extrapolated back to the visible when retrieving water leaving radiances from remotely sensed data. For the turbid coastal environment, the water leaving radiances for channels between 0.66 and 0.87 micrometer may not be close to zero because of back scattering by suspended materials in the water. Under these conditions, the channels are no longer useful for deriving information on atmospheric aerosols. As a result, the algorithms developed for applications to clear ocean waters cannot be easily modified to retrieve water leaving radiances from remote sensing data acquired over the costal environments. We have recently developed a fast and fully functional atmospheric correction algorithm for hyperspectral remote sensing of ocean color with the Coastal Ocean Imaging Spectrometer (COIS). Our algorithm uses lookup tables generated with a vector radiative transfer code developed by Ahmad and Fraser (1982) and a spectral matching technique for the retrieval of water leaving radiances. The information on atmospheric aerosols is estimated using dark channels beyond 0.86 micron. Quite reasonable results were obtained when applying the algorithm to process spectral imaging data acquired over Chesapeake Bay with the NASA JPL Airborne Visible Infrared Imaging Spectrometer (AVIRIS).
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