Propagation of sensor noise in oceanic hyperspectral remote sensing.

In previous works, the authors have shown via numerical simulation that sensor noise, even assuming otherwise perfect knowledge of the environment, can cause large scale variations in the retrieval of concentrations of biophysical parameters in a water body, and also investigated methods for using statistical measures (such as the Mahalanobis distance) to help mitigate these issues. In this work, we derive explicit formulas that can be used to estimate how uncertainty in the sensor radiance is propagated to uncertainty in the remote sensing reflectanceRrs(λ), without the need for simulations. In particular, the formulas show that the variation in Rrs(λ)is affected by not only the noise characteristics of the sensor, but also by the conditions (atmospheric parameters, viewing angles, altitude) under which the data is collected. We include validation results for the formulas over a wide range of atmospheric conditions, and show by example how the collection conditions can affect the uncertainty in Rrs(λ).

Extracting flat-field images from scene-based image sequences using phase correlation.

Flat-field image processing is an essential step in producing high-quality and radiometrically calibrated images. Flat-fielding corrects for variations in the gain of focal plane array electronics and unequal illumination from the system optics. Typically, a flat-field image is captured by imaging a radiometrically uniform surface. The flat-field image is normalized and removed from the images. There are circumstances, such as with remote sensing, where a flat-field image cannot be acquired in this manner. For these cases, we developed a phase-correlation method that allows the extraction of an effective flat-field image from a sequence of scene-based displaced images. The method uses sub-pixel phase correlation image registration to align the sequence to estimate the static scene. The scene is removed from sequence producing a sequence of misaligned flat-field images. An average flat-field image is derived from the realigned flat-field sequence.

Wavelength dependence of the bidirectional reflectance distribution function (BRDF) of beach sands.

The wavelength dependence of the dominant directional reflective properties of beach sands was demonstrated using principal component analysis and the related correlation matrix. In general, we found that the hyperspectral bidirectional reflectance distribution function (BRDF) of beach sands has weak wavelength dependence. Its BRDF varies slightly in three broad wavelength regions. The variations are more evident in surfaces of greater visual roughness than in smooth surfaces. The weak wavelength dependence of the BRDF of beach sand can be captured using three broad wavelength regions instead of hundreds of individual wavelengths.

Airborne system for multispectral, multiangle polarimetric imaging.

In this paper, we describe the design, fabrication, calibration, and deployment of an airborne multispectral polarimetric imager. The motivation for the development of this instrument was to explore its ability to provide information about water constituents, such as particle size and type. The instrument is based on four 16 MP cameras and uses wire grid polarizers (aligned at 0°, 45°, 90°, and 135°) to provide the separation of the polarization states. A five-position filter wheel provides for four narrow-band spectral filters (435, 550, 625, and 750 nm) and one blocked position for dark-level measurements. When flown, the instrument is mounted on a programmable stage that provides control of the view angles. View angles that range to ±65° from the nadir have been used. Data processing provides a measure of the polarimetric signature as a function of both the view zenith and view azimuth angles. As a validation of our initial results, we compare our measurements, over water, with the output of a Monte Carlo code, both of which show neutral points off the principle plane. The locations of the calculated and measured neutral points are compared. The random error level in the measured degree of linear polarization (8% at 435) is shown to be better than 0.25%.

Vicarious calibrations of HICO data acquired from the International Space Station.

The Hyperspectral Imager for the Coastal Ocean (HICO) presently onboard the International Space Station (ISS) is an imaging spectrometer designed for remote sensing of coastal waters. The instrument is not equipped with any onboard spectral and radiometric calibration devices. Here we describe vicarious calibration techniques that have been used in converting the HICO raw digital numbers to calibrated radiances. The spectral calibration is based on matching atmospheric water vapor and oxygen absorption bands and extraterrestrial solar lines. The radiometric calibration is based on comparisons between HICO and the EOS/MODIS data measured over homogeneous desert areas and on spectral reflectance properties of coral reefs and water clouds. Improvements to the present vicarious calibration techniques are possible as we gain more in-depth understanding of the HICO laboratory calibration data and the ISS HICO data in the future.

A dual-spectrometer approach to reflectance measurements under sub-optimal sky conditions.

This paper presents a practical method for the development of spectral reflectance libraries under sub-optimal sky conditions. Although there are commercially available spectrometers which simultaneously measure both downwelling and upwelling radiance to mitigate the impact of sub-optimal sky conditions, these spectrometers only record in the visible and near infra-red. There are presently no commercially available spectrometers with this capability that can record the visible through short-wave infra-red. This paper presents a practical method of recording and processing data using coordinated measurements from two full-range spectrometers and discusses potential pitfalls and solutions required to achieve accurate reflectance spectra. Results demonstrate that high-quality spectral reflectance libraries can be developed with this approach.

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

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.