Robert A. Leathers

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.

Ocean PHILLS hyperspectral imager: design, characterization, and calibration

The Ocean Portable Hyperspectral Imager for Low-Light Spectroscopy (Ocean PHILLS) is a hyperspectral imager specifically designed for imaging the coastal ocean. It uses a thinned, backsideilluminated CCD for high sensitivity and an all-reflective spectrograph with a convex grating in an Offner configuration to produce a nearly distortionfree image. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several oceanographic experiments in 1999-2001. Here we describe the instrument design and present the results of laboratory characterization and calibration. We also present examples of remote-sensing reflectance data obtained from the LEO-15 site in New Jersey that agrees well with ground-truth measurements.

Derivative analysis of absorption features in hyperspectral remote sensing data of carbonate sediments

This study uses derivative spectroscopy to assess qualitative and quantitative information regarding seafloor types that can be extracted from hyperspectral remote sensing reflectance signals. Carbonate sediments with variable concentrations of microbial pigments were used as a model system. Reflectance signals measured directly over sediment bottoms were compared with remotely sensed data from the same sites collected using an airborne sensor. Absorption features associated with accessory pigments in the sediments were lost to the water column. However major sediment pigments, chlorophyll a and fucoxanthin, were identified in the remote sensing spectra and showed quantitative correlation with sediment pigment concentrations. Derivative spectra were also used to create a simple bathymetric algorithm.

Self-shading correction for upwelling sea-surface radiance measurements made with buoyed instruments.

Upwelling radiance measurements made with instruments designed to float at the sea surface are shaded both by the instrument housing and by the buoy that holds the instrument. The amount of shading is wavelength dependent and is affected by the local marine and atmospheric conditions. Radiance measurements made with such instruments should be corrected for this self-shading error before being applied to remote sensing calibrations or remote sensing algorithm validation. Here we use Monte Carlo simulations to compute the self-shading error of a commercially available buoyed radiometer so that measurements made with this instrument can be improved. This approach can be easily adapted to the dimensions of other instruments.

Scene-based nonuniformity corrections for optical and SWIR pushbroom sensors

We propose and evaluate several scene-based methods for computing nonuniformity corrections for visible or near-infrared pushbroom sensors. These methods can be used to compute new nonuniformity correction values or to repair or refine existing radiometric calibrations. For a given data set, the preferred method depends on the quality of the data, the type of scenes being imaged, and the existence and quality of a laboratory calibration. We demonstrate our methods with data from several different sensor systems and provide a generalized approach to be taken for any new data set.

Self-shading correction for oceanographic upwelling radiometers

We present the derivation of an analytical model for the self-shading error of an oceanographic upwelling radiometer. The radiometer is assumed to be cylindrical and can either be a profiling instrument or include a wider cylindrical buoy for floating at the sea surface. The model treats both optically shallow and optically deep water conditions and can be applied any distance off the seafloor. We evaluate the model by comparing its results to those from Monte Carlo simulations. The analytical model performs well over a large range of environmental conditions and provides a significant improvement to previous analytical models. The model is intended for investigators who need to apply self-shading corrections to radiometer data but who do not have the ability to compute shading corrections with Monte Carlo simulations. The model also can provide guidance for instrument design and cruise planning.

A Novel Method for Illumination Suppression in Hyperspectral Images

We have proposed a new method for illumination suppression in hyperspectral image data. This involves transforming the data into a hyperspherical coordinate system, segmenting the data cloud into a large number of classes according to the radius dimension, and then demeaning each class, thereby eliminating the distortion introduced by differential absorption in shaded regions. This method was evaluated against two other illumination-suppression methods using two metrics: visual assessment and spectral similarity of similar materials in shaded and fully illuminated regions. The proposed method shows markedly superior performance by each of these metrics.

Analysis of a point-source integrating-cavity absorption meter

We evaluate the theoretical performance of a point-source integrating-cavity absorption meter (PSICAM) with Monte Carlo simulations and a sensitivity analysis. We quantify the scattering errors, verifying that they are negligible for most ocean optics applications. Although the PSICAM detector response is highly sensitive to the value of the wall reflectivity, the absorption of an unknown fluid can be accurately determined with a PSICAM if appropriate reference solution(s) are chosen. We also quantify the error that results if the source is not perfectly isotropic, finding that moderate amounts of source anisotropy can be tolerated provided that the detector is properly located with respect to the source.

Automatic ship detection from commercial multispectral satellite imagery

Commercial multispectral satellite sensors spend much of their time over the oceans. NRL has demonstrated an automatic processing system for finding ships at sea using commercially available multispectral data. To distinguish ships from whitecaps and clouds, a water/cloud clutter subspace is estimated and a continuum fusion derived anomaly detection algorithm is applied. This provides a maritime awareness capability with an acceptable detection rate while maintaining a low rate of false alarms. The system also provides a confidence metric, which can be used to further limit the false alarm rate.

Dynamic thresholding for hyperspectral shadow detection using Levenberg-Marquardt minimization on multiple Gaussian illumination distributions

Irregular illumination across a hyperspectral image makes it difficult to detect targets in shadows, perform change detection, and segment the contents of the scene. To correct for the data in shadow, we first convert the data from Cartesian space to a hyperspherical coordinate system. Each N-dimensional spectral vector is converted to N-1 spectral angles and a magnitude representing the illumination value of the spectra. Similar materials will have similar angles and the differences in illumination will be described mostly by the magnitude. In the data analyzed, we found that the distribution of illumination values is well approximated by the sum of two- Gaussian distributions, one for shadow and one for non-shadow. The Levenberg-Marquardt algorithm is used to fit the empirical illumination distribution to the theoretical Gaussian sum. The LM algorithm is an iterative technique that locates the minimum of a multivariate function that is expressed as the sum of squares of non-linear real-valued functions. Once the shadow and non-shadow distributions have been modeled, we find the optimal point to be one standard deviation out on the shadow distribution, allowing for the selection of about 84% of the shadows. This point is then used as a threshold to decide if the pixel is shadow or not. Corrections are made to the shadow regions and a spectral matched filter is applied to the image to test target detection in shadow regions. Results show a signal-to-noise gain over other illumination suppression techniques.