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Spectral data offer a means of estimating the critical parameters of sediments, including sediment composition, moisture content, surface roughness, density, and grain-size distribution. Macroscopic surface roughness in particular has a substantial impact on the structure of the bidirectional reflectance factor (BRF) and the angular distribution of scattered light. In developing the models to invert the properties of the surface beyond just surface composition, roughness must also be accounted for in order to achieve reliable and repeatable results. This paper outlines laboratory studies in which the BRF and surface digital elevation measurements were performed on dry clay sediments. The results were used to explore the suitability of various roughness metrics to account for the radiometric effect of surface roughness. The metrics that are specifically addressed in this paper include random roughness and sill variance. Relative accuracy and tradeoffs between these metrics are described. We find that spectral variability, especially near spectral absorption features, correlates strongly with the quantified measures of surface roughness. We also find that spectral variability is sensitive to the sensor fore-optic size. The results suggest that roughness parameters might be directly determined from the spectrum itself. The relationship between spectral variability and macroscopic surface roughness was particularly strong in some broad spectral ranges of the visible, near infrared, and shortwave infrared, including the near-infrared region between 600 and 850 nm.

We measured and compared hyperspectral bidirectional reflectance factors of a sand sample of varying roughness levels using the Goniometer of the Rochester Institute of Technology-Two. We developed a geotechnical routine to generate sand samples of approximately constant density and grain size distribution, but varying roughness levels. In addition, we developed sand samples of two different classes of surface roughness: wave-like and normally distributed. The samples exhibiting normally distributed roughness met several criterion outlined by Hapke in the roughness correction to his photometric model for a smooth sediment surface. We developed a method to empirically forward model the photometric model for a rough surface using roughness metrics derived from digital elevation models of the surface. Our results from empirically forward modeling the correction factor indicate that Hapke's shadowing function does not adequately describe the effects of macroscopic roughness at a subcentimeter scale. In addition, we also performed experiments in which we oriented the surface waves of the wave-like roughness profiles in different orientations to the principal plane of illumination. These results indicate that future photometric models of wave-like roughness should include a description of how multiple scattering increases reflectance within cavities, reducing the effects of shadowing within the cavities. Our results also suggest that since Hapke's model correction for macroscopic roughness assumes that the underlying distribution of surface slopes does not depend on azimuth, it cannot adequately characterize surface roughness when it is both structured and has a preferred orientation.

In this paper, we examine the accuracy of manifold coordinate representations as a reduced representation of a hyperspectral imagery (HSI) lookup table (LUT) for bathymetry retrieval. We also explore on a more limited basis the potential for using these coordinates for modeling other in water properties. Manifold coordinates are chosen because they are a data-driven intrinsic set of coordinates, which naturally parameterize nonlinearities that are present in HSI of water scenes. The approach is based on the extraction of a reduced dimensionality representation in manifold coordinates of a sufficiently large representative set of HSI. The manifold coordinates are derived from a scalable version of the isometric mapping algorithm. In the present and in our earlier works, these coordinates were used to establish an interpolating LUT for bathymetric retrieval by associating the representative data with ground truth data, in this case from a Light Detection and Ranging (LIDAR) estimate in the representative area. While not the focus of the present paper, the compression of LUTs could also be applied, in principle, to LUTs generated by forward radiative transfer models, and some preliminary work in this regard confirms the potential utility for this application. In this paper, we analyze the approach using data acquired by the Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) hyperspectral camera over the Indian River Lagoon, Florida, in 2004. Within a few months of the PHILLS overflights, Scanning Hydrographic Operational Airborne LIDAR Survey LIDAR data were obtained for a portion of this study area, principally covering the beach zone and, in some instances, portions of contiguous river channels. Results demonstrate that significant compression of the LUTs is possible with little loss in retrieval accuracy.

In recent publications, we have presented a data-driven approach to representing the nonlinear structure of hyperspectral imagery using manifold coordinates. The approach relies on graph methods to derive geodesic distances on the high-dimensional hyperspectral data manifold. From these distances, a set of intrinsic manifold coordinates that parameterizes the data manifold is derived. Scaling the solution relied on divide-conquer-and-merge strategies for the manifold coordinates because of the computational and memory scaling of the geodesic coordinate calculations. In this paper, we improve the scaling performance of isometric mapping (ISOMAP) and achieve full-scene global manifold coordinates while removing artifacts generated by the original methods. The CPU time of the enhanced ISOMAP approach scales as O(Nlog 2(N)), where N is the number of samples, while the memory requirement is bounded by O(Nlog(N)). Full hyperspectral scenes of O(10 6) samples or greater are obtained via a reconstruction algorithm, which allows insertion of large numbers of samples into a representative "backbone" manifold obtained for a smaller but representative set of O(105) samples. We provide a classification example using a coastal hyperspectral scene to illustrate the approach

A new algorithm for exploiting the nonlinear structure of hyperspectral imagery is developed and compared against the de facto standard of linear mixing. This new approach seeks a manifold coordinate system that preserves geodesic distances in the high-dimensional hyperspectral data space. Algorithms for deriving manifold coordinates, such as isometric mapping (ISOMAP), have been developed for other applications. ISOMAP guarantees a globally optimal solution, but is computationally practical only for small datasets because of computational and memory requirements. Here, we develop a hybrid technique to circumvent ISOMAP's computational cost. We divide the scene into a set of smaller tiles. The manifolds derived from the individual tiles are then aligned and stitched together to recomplete the scene. Several alignment methods are discussed. This hybrid approach exploits the fact that ISOMAP guarantees a globally optimal solution for each tile and the presumed similarity of the manifold structures derived from different tiles. Using land-cover classification of hyperspectral imagery in the Virginia Coast Reserve as a test case, we show that the new manifold representation provides better separation of spectrally similar classes than one of the standard linear mixing models. Additionally, we demonstrate that this technique provides a natural data compression scheme, which dramatically reduces the number of components needed to model hyperspectral data when compared with traditional methods such as the minimum noise fraction transform.

A credit assignment approach to decision-based classifier fusion is developed and applied to the problem of land-cover classification from multiseason airborne hyperspectral imagery. For each input sample, the new method uses a smoothed estimated reliability measure (SERM) in the output domain of the classifiers. SERM requires no additional training beyond that needed to optimize the constituent classifiers in the pool, and its generalization (test) accuracy exceeds that of a number of other extant methods for classifier fusion. Hyperspectral imagery from HyMAP and PROBE2 acquired at three points in the growing season over Smith Island, VA, a barrier island in the Nature Conservancy's Virginia Coast Reserve, serves as the basis for comparing SERM with other approaches.

In the past, "active learning" strategies have been proposed for improving the convergence and accuracy of statistical classifiers. However, many of these approaches have large storage requirements or unnecessarily large computational burdens and, therefore, have been impractical for the large-scale databases typically found in remote sensing, especially hyperspectral applications. In this paper, we develop a practical on-line approach with only modest storage requirements. The new approach improves the convergence rate associated with the optimization of adaptive classifiers, especially in high-dimensional remote sensing data. We demonstrate the new approach using PROBE2 hyperspectral imagery and find convergence time improvements of two orders of magnitude in the optimization of land-cover classifiers.

Automatic land cover classification maps were developed from Airborne Hyperspectral Scanner (HyMAP) imagery acquired May 8, 2000 over Smith Island, VA, a barrier island in the Virginia Coast Reserve. Both unsupervised and supervised classification approaches were used to create these products to evaluate relative merits and to develop models that would be useful to natural resource managers at higher spatial resolution than has been available previously. Ground surveys made by us in late October and early December 2000 and again in May, August, and October 2001 and May 2002 provided ground truth data for 20 land cover types. Locations of pure land cover types recorded with global positioning system (GPS) data from these surveys were used to extract spectral end-members for training and testing supervised land cover classification models. Unsupervised exploratory models were also developed using spatial-spectral windows and projection pursuit (PP), a class of algorithms suitable for extracting multimodal views of the data. PP projections were clustered by ISODATA to produce an unsupervised classification. Supervised models, which relied on the GPS data, used only spectral inputs because for some categories in particular areas, labeled data consisted of isolated single-pixel waypoints. Both approaches to the classification problem produced consistent results for some categories such as Spartina alterniflora, although there were differences for other categories. Initial models for supervised classification based on 112 HyMAP spectra, labeled in ground surveys, obtained reasonably consistent results for many of the dominant categories, with a few exceptions.

Results are presented for an experiment utilizing a pastoral land scene with a variety of eight classes, imaged by the NRL dual band (X and L) polarimetric synthetic aperture radar (NUWSAR) at a spatial resolution of 1.2 m. Projection pursuit (PP) statistical analysis tools were applied to a set of simultaneous L-band and X-band fully polarized images (six independent channels) to demonstrate the utility of land classification at high spatial resolution from a light aircraft using SAR. The statistical confusion matrix was used as a quantitative optimization measure of classification. Samples of eight classes from a portion of the scene were used to define a training set, then PP tools were used for classification. It is clear that L-band and X-band fully polarized data view the classes in a significantly different manner, and each brings independent information to the analysis. These results are not meant to be exhaustive at this time but to demonstrate the utility of applying PP tools to multiband and polarization SAR data and to give an indication of the quality of classification one can achieve with moderately high spatial resolution SAR data using a light plane platform.