Trijntje Valerie Downes

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.

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.

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.

Median spectral-spatial bad pixel identification and replacement for hyperspectral SWIR sensors

Hyperspectral focal plane arrays typically contain many pixels that are excessively noisy, dead, or exhibit poor signal to- noise performance in comparison to the average pixel. These bad pixels can significantly impair the performance of spectral target-detection algorithms. Even a single missed bad pixel can lead to false alarms. If the bad pixels are sparsely populated across the focal plane, the over-sampling in both spatial and spectral dimensions of the array can be capitalized upon to replace these pixels without significant loss of information. However, bad pixels are frequently localized in clusters, requiring a replacement strategy that rather than providing a good estimate of the missing data will instead minimize artifacts that may negatively affect the performance of spectral detection algorithms. In this paper, we evaluate a robust method to automatically identify bad pixels for short-wavelength infrared (SWIR) hyperspectral sensors. In addition, we introduce a novel procedure for the replacement of these pixels, which we demonstrate provides a better estimate of the original pixel value compared to interpolation methods for bad pixels found as both isolated individuals and in clusters. The advantages of our technique are discussed and demonstrated with data from several different airborne sensor systems.

Signature evolution with covariance equalization in oblique hyperspectral imagery

Covariance equalization (CE) is a method by which one can predict the change in an objects hyperspectral signature due to changes in sun position, atmospheric conditions, and viewing angle and range. Specifically, CE produces a linear transformation that relates the objects signature as measured at the sensor at a particular time to that measured at another time and under different conditions. The transformation is based on the background statistics of a scene imaged at the two times. Although CE was derived under the assumption that the two images cover mostly the same geographic area, it also has been found to work well for objects that have moved from one location to another. The CE technique has been previously verified with data from a nadir-viewing visible hyperspectral camera. In this paper, however, we show results from the application of CE to highly oblique hyperspectral SWIR data. We evaluate the utility of CE primaily through its effectiveness in transforming signatures acquired under one set of conditions for application to matched-filter object detection under a second set of conditions (e.g., view angle, slant range, altitude, atmospheric conditions, and time of day). Object detection with highly oblique sensors (75 deg. to 80 deg. off-nadir) is far more difficult than with nadir-viewing sensors for several reasons: increased atmospheric optical thickness, which results in lower signal-to-noise and higher adjacency effects; fewer pixels on object; the effects of the nonuniformity of the bidirection reflectance function of most man-made objects; and the change in pixel size when measurements are taken at different slant ranges.

Stable Scene-based Non-uniformity Correction Coefficients for Hyperspectral SWIR Sensors

Scene-based non-uniformity correction (NUC) methods commonly produce artifacts as a result of NUC coefficient biasing by the specific scene content (e.g., streaking at high-contrast boundaries). We propose and evaluate a new scene-based method for computing stable non-uniformity correction coefficients for short-wavelength infrared (SWIR) scanning hyperspectral sensors relying on the spatial ratio of spectral ratios to eliminate bias from the image scene. The new technique produces NUC coefficients computed from scene data that converge more quickly and remain more stable than other methods, resulting in calibrated images without scene-induced artifacts. Advantages of our technique are discussed and demonstrated with data from several different airborne sensor systems.