Ronald G. Resmini

A comparison study of dimension estimation algorithms

The inherent dimension of hyperspectral data is commonly estimated for the purpose of dimension reduction. However, the dimension estimate itself may be a useful measure for extracting information about hyperspectral data, including scene content, complexity, and clutter. There are many ways to estimate the inherent dimension of data, each measuring the data in a different way. This paper compares a group of dimension estimation metrics on a variety of data, both full scene and individual material regions, to determine the relationship between the different estimates and what features each method is measuring when applied to complex data.

Simultaneous spectral/spatial detection of edges for hyperspectral imagery: the HySPADE algorithm revisited

The hyperspectral/spatial detection of edges (HySPADE) algorithm, originally published in 2004 [1], has been modified and applied to a wider diversity of hyperspectral imagery (HSI) data. As originally described in [1], HySPADE operates by converting the naturally two-dimensional edge detection process based on traditional image analysis methods into a series of one-dimensional edge detections based on spectral angle. The HySPADE algorithm: i) utilizes spectral signature information to identify edges; ii) requires only the spectral information of the HSI scene data and does not require a spectral library nor spectral matching against a library; iii) facilitates simultaneous use of all spectral information; iv) does not require endmember or training data selection; v) generates multiple, independent data points for statistical analysis of detected edges; vi) is robust in the presence of noise; and vii) may be applied to radiance, reflectance, and emissivity data--though it is applied to radiance and reflectance spectra (and their principal components transformation) in this report. HySPADE has recently been modified to use Euclidean distance values as an alternative to spectral angle. It has also been modified to use an N x N-pixel sliding window in contrast to the 2004 version which operated only on spatial subset image chips. HySPADE results are compared to those obtained using traditional (Roberts and Sobel) edge-detection methods. Spectral angle and Euclidean distance HySPADE results are superior to those obtained using the traditional edge detection methods; the best results are obtained by applying HySPADE to the first few, information-containing bands of principal components transformed data (both radiance and reflectance). However, in practice, both the Euclidean distance and spectral angle versions of HySPADE should be applied and their results compared. HySPADE results are shown; extensions of the HySPADE concept are discussed as are applications for HySPADE in HSI analysis and exploitation.

Hyperspectral imagery and LiDAR for geological analysis of Cuprite, Nevada

Fusion of Light Detection and Ranging (LiDAR) and Hyperspectral Imagery (HSI) products is useful for geological analysis, particularly for visualization of geomorphology and hydrology. In early 2007, coincident hyperspectral imagery and LiDAR were acquired over Cuprite, Nevada. The data were analyzed with ENVI and the ENVI LiDAR Toolkit. Results of the analysis of these data suggest, for some surfaces, a correlation between mineral content and surface roughness. However, the LiDAR resolution (~1 meter ground sampling distance) is likely too coarse to extract surface texture properties of clay minerals in some of the alluvial fans captured in the imagery. Though not demonstrated in this particular experiment (but a goal of the research), the relation between surface roughness and mineral composition may provide valuable information about the mechanical properties of the surface cover-in addition to generating another variable useful for material characterization, image classification, and scene segmentation. Future mission planning should include consideration of determining optimal ground sampling to be used by LiDAR and HSI systems. The fusion of LiDAR elevation data and multi- and hyperspectral classification results is, in and of itself, a valuable tool for imagery analysis and should be explored further.

Hyperspectral trace gas detection using the wavelet packet transform

A method for trace gas detection in hyperspectral data is demonstrated using the wavelet packet transform. This new method, the Wavelet Packet Subspace (WPS), applies the wavelet packet transform and selects a best basis for pattern matching. The wavelet packet transform is an extension of the wavelet transform, which fully decomposes a signal into a library of wavelet packet bases. Application of the wavelet packet transform to hyperspectral data for the detection of trace gases takes advantage of the ability of the wavelet transform to locate spectral features in both scale and location. By analyzing the wavelet packet tree of specific gas, nodes of the tree are selected which represent an orthogonal best basis. The best basis represents the significant spectral features of that gas. This is then used to identify pixels in the scene using existing matching algorithms such as spectral angle or matched filter. Using data from the Airborne Hyperspectral Imager (AHI), this method is compared to traditional matched filter detection methods. Initial results demonstrate a promising wavelet packet subspace technique for hyperspectral trace gas detection applications.

Comparison of spectral matching techniques for vegetation species delineation of the National Arboretum

Identification of differing vegetation species has been a lauded ability of hyperspectral imagery and analysis but continues to be a challenging problem. Hyperspectral imagery has been used for years in applications such as vegetation analysis and delineation, terrain categorization, explosive mine detection, environmental impacts and effects, and agriculture and crop evaluation. Unlike applications which focus on detection of specific targets with constant spectral signatures, vegetation signatures continually vary across their growth cycle. In order to identify various vegetation species, either large collections of time-varying reference signatures are required, or ground truth/training data is needed. These are not always viable options and in many cases only in-scene data can be used. In this study we compare the performance of various spectral matching methods in separating vegetation at the species level. Parametric, non-parametric, derivative techniques, and other methods are compared. These methods are applied to a complex scene, the National Arboretum in Washington DC, which was imaged by an airborne hyperspectral sensor in August, 2008. This survey assesses performance of spectral matching methods for vegetation species delineation and makes recommendations for its application in hyperspectral data analysis.

Detection of Sulfur Dioxide in AIRS Data With the Wavelet Packet Subspace

A generalized method for trace-gas detection in hyperspectral data using the wavelet packet transform is being developed. This new method decomposes the input signal using a wavelet packet transform. A best basis that is optimized for the target signature is selected for pattern matching. The wavelet packet transform, an extension of the wavelet transform, fully decomposes a signal into a library of wavelet packet bases. The application of the wavelet packet transform to hyperspectral data for the detection of trace gases takes advantage of the ability of the wavelet transform to locate spectral features in both scale and location. By analyzing the wavelet packet tree of a specific target gas, the nodes of the tree that represent an orthogonal best basis are selected. The best basis represents the significant spectral features of that gas. This is then used to identify pixels in the scene using existing matching algorithms such as spectral angle. Using data from NASAs Advanced Infrared Sounder, this method is used to detect sulfur dioxide. Initial results demonstrate a promising wavelet-packet-subspace technique for trace-gas-detection applications.

Modeling linear and intimate mixtures of materials in hyperspectral imagery with single-scattering albedo and kernel approaches

Abstract. Linear mixtures of materials in a scene often occur because the resolution of a sensor is relatively coarse, resulting in pixels containing patches of different materials within them. This phenomenon causes nonoverlapping areal mixing and can be modeled by a linear mixture model. More complex phenomena, such as the multiple scattering in mixtures of vegetation, soils, granular, and microscopic materials within pixels can result in intimate mixing with varying degrees of nonlinear behavior. In such cases, a linear model is not sufficient. This study considers two approaches for unmixing pixels in a scene that may contain linear or intimate (nonlinear) mixtures. The first method is based on earlier studies that indicate nonlinear mixtures in reflectance space are approximately linear in albedo space. The method converts reflectance to single-scattering albedo according to Hapke theory and uses a constrained linear model on the computed albedo values. The second method is motivated by the same idea, but uses a kernel that seeks to capture the linear behavior of albedo in nonlinear mixtures of materials. This study compares the two approaches and pays particular attention to these dependencies. Both laboratory and airborne collections of hyperspectral imagery are used to validate the methods.

Hyperspectral imagery transformations using real and imaginary features for improved classification

Several studies have reported that the use of derived spectral features, in addition to the original hyperspectral data, can facilitate the separation of similar classes. Linear and nonlinear transformations are employed to project data into mathematical spaces with the expectation that the decision surfaces separating similar classes become well defined. Therefore, the problem of discerning similar classes in expanded space becomes more tractable. Recent work presented by one of the authors discusses a dimension expansion technique based on generating real and imaginary complex features from the original hyperspectral signatures. A complex spectral angle mapper was employed to classify the data. In this paper, we extend this method to include other approaches that generate derivative-like and wavelet-based spectral features from the original data. These methods were tested with several supervised classification methods with two Hyperspectral Image (HSI) cubes.

Spectral-Density-Based Graph Construction Techniques for Hyperspectral Image Analysis

The past decade has seen the emergence of many hyperspectral image (HSI) analysis algorithms based on graph theory and derived manifold coordinates. The performance of these algorithms is inextricably tied to the graphical model constructed from the spectral data, i.e., the community structure of the spectral data must be well represented to extract meaningful information. This paper provides a survey of many spectral graph construction techniques currently used by the hyperspectral community and discusses their advantages and disadvantages for hyperspectral analyses. A focus is provided on techniques influenced by spectral density from which the concept of community structure arises. Two inherently density-weighted graph construction techniques from the data mining literature, shared nearest neighbor (NN) and mutual proximity, are also introduced and compared as they have not been previously employed in HSI analyses. Density-based edge allocation is demonstrated to produce more uniform NN lists than nondensity-based techniques by demonstrating an increase in the number of intracluster edges and improved

A new approach to infer surface emissivity parameters from longwave infrared hyperspectral measurements

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