The purpose of this work is to measure changes in deciduous tree reflectance spectra as a function of time from late summer to autumn senescence. Leaves were harvested from two maple trees growing in eastern Massachusetts. Reflectance in the 350-2500 nm range was measured in the laboratory on stacks of freshly-harvested leaves. We calculated a number of published spectral indices, finding that most of the indices varied remarkably little across the time period. In some case, the measurement uncertainty was small, although the measurements exhibited wide scatter over the time period. The Normalized Difference Vegetation Index showed only a slight downward drift throughout the measurement period. The red edge wavelength was observed to decrease dramatically from the summer growth period (~725 nm) to autumn senescence (~700 nm).
The statistics of natural backgrounds extracted from an Airborne Visible and Infrared Imaging Spectrometer (AVIRIS)
hyperspectral datacube collected over Fort AP Hill, VA, were used to demonstrate the effects of the two atmospheric
components of a statistical end-to-end performance prediction model. New capabilities in MODTRANTM5 were used to
generate coefficients for linear transformations used in the atmospheric transmission and compensation components of a
typical end-to-end model. Model radiance statistics, calculated using reflectance data, is found to be similar to the
original AVIRIS radiance data. Moreover, if identical atmospheric conditions are applied in the atmospheric
transmission and in the atmospheric compensation model components and the effects of sensor noise are disregarded, the
resulting reflectance statistics are identical to the original reflectance statistics.
We describe a new approach to unsupervised classification that automatically finds dense parts of the hyperspectral data cloud. These dense regions are identified as the cluster centers required for unsupervised classification. The approach is tested using AVIRIS hyperspectral imagery from central Texas that has spectrally well separated land covers. The algorithm is then applied to the more stressing case of separating coniferous and deciduous forests in eastern Virginia. We find that the major spectral difference is brighter reflectance in the NIR plateau for deciduous forests compared to adjacent coniferous stands. This difference is sufficient to distinguish the forest types, and is confirmed by comparison to ground truth information.
The objective of this paper is to investigate the effects of dimensionality reduction on the statistical distribution of natural hyperspectral backgrounds. The statistical modeling is based on application of the multivariate t-elliptically contoured distribution to background regions which have been shown to exhibit "long-tail" behavior. Hyperspectral backgrounds are commonly represented with reduced dimensionality in order to minimize statistical redundancies in the spectral dimension and to satisfy data processing and storage requirements. In this investigation, we extend the statistical characterization of these backgrounds by modeling their Mahalanobis distance distributions in reduced dimensional space. The dimensionality reduction techniques applied in this paper include Principal Components Analysis (PCA) and spectral band aggregation. The knowledge gained from a better understanding of the effects of dimensionality reduction will be beneficial toward improving threshold selection for target detection applications. These investigations are done using hyperspectral data from the AVIRIS sensor and include spectrally homogeneous regions of interest obtained by visual interactive spatial segmentation.
The objective of this paper is the statistical characterization of
natural hyperspectral backgrounds using the multivariate t-elliptically contoured distribution. Traditionally, hyperspectral backgrounds have been modeled using multivariate Gaussian distributions; however it is well known that real data often exhibit "long-tail" behavior that cannot be accounted by normal distribution models. The proposed multivariate t-distribution model has elliptical equiprobability contours whose center and ellipticity is specified by the mean vector and covariance matrix of the data. The density of the contours, which is reflected into the distribution of the Mahalanobis distance, is controlled by an extra parameter, the number of degrees of freedom. As the number of degrees of freedom increases, the tails decrease and approach those of a normal distribution with the same mean and covariance. In this work we investigate the application of t-elliptically contoured distributions to the characterization of different hyperspectral background data obtained by visually interactive spatial segmentation ("physically" homogeneous classes), automated clustering algorithms using spectral similarity metrics (spectrally
homogeneous classes), and by fitting normal mixture models (statistically homogeneous classes). These investigations are done
using hyperspectral data from the AVIRIS sensor.
We describe development of a background spectral library for Fort A. P. Hill, located in northeastern Virginia, based on hyperspectral images and an extensive land cover database. The database was used to identify areas of uniform land cover. The library contains means and standard deviations for 15 spectra measured in these uniform areas. Terrain categorization products consist of classification maps and fractional abundance maps determined by linear mixture analysis. There is excellent qualitative agreement between the linear unmixing results and the known land covers.
Image pixels represent either distinct materials (end members) that are present in the image, or mistures of two or more of these pure materials. Estimates of pure end member spectra are needed for spectral libraries and for input into pixel unmixing codes. We investigate three algorithms for estimating end member spectra: (1) the convex hull method in which an n-dimensional surface is shrink- wrapped around the data cloud; (2) a pixel-by-pixel search method in which pixels that have sufficiently different spectral angles are declared end members; (3) a pixel-by- pixel search method using Euclidean distance as a measure, followed by clustering to improve the estimate of the spectra. The convex hull technique should provide an estimate of pure end member spectra while the pixel-by-pixel search methods should find both distinct end members and distinct mixtures. Each method requires user-set thresholds to find distinct spectra, which can be expressed in spectral angle degrees or image-dependent units for Euclidean distance. Estimates for the lower threshold (below which two spectra are considered to be the same material) and the upper threshold (above which two spectra are definitely different materials) are derived empirically. Low-altitude AVIRIS data will be used to demonstrate the utility of these end member extraction methods. We will illusxtrate how well each technique compare to the other, and compare how well individual algorithms work across adjacent scenes.