Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

/pdf/hyperspectral-subspace-identification-4cvaygfbm7.pdf

Hyperspectral Subspace Identification

This letter presents a framework of composite kernel machines for enhanced classification of hyperspectral images. This novel method exploits the properties of Mercer's kernels to construct a family of composite kernels that easily combine spatial and spectral information. This framework of composite kernels demonstrates: 1) enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only: 2) flexibility to balance between the spatial and spectral information in the classifier; and 3) computational efficiency. In addition, the proposed family of kernel classifiers opens a wide field for future developments in which spatial and spectral information can be easily integrated.

Composite kernels for hyperspectral image classification

Aim To produce a statistical stratification of the European environment, suitable for stratified random sampling of ecological resources, the selection of sites for representative studies across the continent, and to provide strata for modelling exercises and reporting. Location A ‘Greater European Window’ with the following boundaries: 11 ° W, 32 ° E, 34 ° N, 72 ° N. Methods Tw enty of the most relevant available environmental variables were selected, based on experience from previous studies. Principal components analysis (PCA) was used to explain 88% of the variation into three dimensions, which were subsequently clustered using an ISODATA clustering routine. The mean first principal component values of the classification variables were used to aggregate the strata into Environmental Zones and to provide a basis for consistent nomenclature. Results The Environmental Stratification of Europe (EnS) consists of 84 strata, which have been aggregated into 13 Environmental Zones. The stratification has a 1 km 2 resolution. Aggregations of the strata have been compared to other European classifications using the Kappa statistic, and show ‘good’ comparisons. The individual strata have been described using data from available environmental databases. The EnS is available for noncommercial use by applying to the corresponding author. Main conclusions The Environmental Stratification of Europe has been constructed using tried and tested statistical procedures. It forms an appropriate stratification for stratified random sampling of ecological resources, the selection of sites for representative studies across the continent and for the provision of strata for modelling exercises and reporting at the European scale.

A climatic stratification of the environment of Europe

We describe a Profiling Optical and Water Return (POWR) system that has been developed and used extensively at sea. The POWR system is a collection of oceanographic instruments used to measure the inherent optical properties (IOPs) of the upper 100m of the ocean while simultaneously collecting up to eight water samples at various depths for chemical and biological analysis. IOPs are local measurements that are directly related to the properties of the water at the depth sampled; hence it is critical that the water samples be taken at the same time and location as the IOPs. Used during three major experiments, the POWR system has proven valuable for relating IOPs to in-water constituents in support of ocean color remote sensing data product validation, optical model validation, and other interdisciplinary programs.

https://ir.library.oregonstate.edu/downloads/zc77sr70m

A profiling optics and water return system for validation and calibration of ocean color imagery

High-resolution spectroscopy using the Portable Hyperspectral Imager for Low- Light Spectroscopy (PHILLS) was applied to the problem of detecting potentially harmful algae blooms in the coastal environment. Data were collected on two aircraft passes, 30-min apart, over the tidally influenced part of the Potomac River. Use of two wavelengths, 0.676 and 0.700 µm, permitted the detection of surface algae accumulations while avoiding the need for atmospheric corrections, which are problematic in Case-2 water. The analysis identified algal accumulations derived from frontal processes, and narrow, linearly coherent streaks, derived from Langmuir circulation. The streaks increased markedly in number between the two passes and formed a two-dimensional pattern across the river, consistent with the advection time of surface material into windrows. The effect of wind on the patches is primarily a local reorganization of the algal material into new streaks. Spectra from within the streaks compared to those from ambient water showed absorption characteristics consistent with the presence of cyanobacteria. This interpretation is reinforced by available in-situ data. This study illustrates the value of high spectral and temporal resolutions in observing the spatial distribution of the algae, in identifying dominant functional groups, and in understanding the response of the algae to physical forcing.

Airborne hyperspectral imaging of cyanobacteria accumulations in the Potomac River

The global optimal solution (GOS) has proven to be very accurate for deriving water surface velocities from contemporaneous image pairs, but previous studies have used shore-based radars or satellite measurements with resolutions on the order of a kilometer or tens of meters to establish this. In contrast, the objective of this paper is to derive a GOS velocity field from infrared (IR) (3–5 $\mu \text{m}$ ) images having a 1-m pixel size. Because IR images can frequently exhibit a low signal-to-noise ratio, a newly developed GOS technique with a local similarity metric is used to retrieve velocities from such low-contrast data sets. To demonstrate the utility of this new method, we use airborne data collected in the tidal Potomac River over 11 days and spatially varying flood, ebb, and slack conditions. The resulting GOS-derived velocity estimates are compared against acoustic Doppler current profiler (ADCP) measurements taken simultaneously with the airborne collections. Velocity magnitudes and directions are found to have GOS versus ADCP correlations of 0.93 and 0.99, respectively. This high correlation suggests that the GOS technique may be applicable to a variety of lower contrast imagery that can now be collected by a low-altitude remotely controlled aircraft.

Airborne Remote Sensing of Surface Velocities in a Tidal River

Suspended particulate matter (SPM) is a key environmental indicator for rivers, estuaries, and coastal waters, which can be calculated from remote sensing reflectance obtained by an airborne or satellite imager. Here, algorithms from prior studies are applied to a dataset of in-situ at surface hyperspectral remote sensing reflectance, collected in three geographic regions representing different water types. These data show the optically inherent exponential nature of the relationship between reflectance and sediment concentration. However, linear models are also shown to provide a reasonable estimate of sediment concentration when utilized with care in similar conditions to those under which the algorithms were developed, particularly at lower SPM values (0 to 20  mg/L). Fifteen published SPM algorithms are tested, returning strong correlations of R2>0.7, and in most cases, R2>0.8. Very low SPM values show weaker correlation with algorithm calculated SPM that is not wavelength dependent. None of the tested algorithms performs well for high SPM values (>30  mg/L), with most algorithms underestimating SPM. A shift toward a smaller number of simple exponential or linear models relating satellite remote sensing reflectance to suspended sediment concentration with regional consideration will greatly aid larger spatiotemporal studies of suspended sediment trends.

Comparison of remote sensing algorithms for retrieval of suspended particulate matter concentration from reflectance in coastal waters

Coordinated flights of two calibrated airborne imaging spectrometers, HYDICE and AVIRIS, were conducted on June 22, 1995 over Lake Tahoe. As part of HYDICE's first operational mission, one objective was to test the system performance over the dark homogeneous target provided by the clear deep waters of the lake. The high altitude and clear atmosphere makes Lake Tahoe a simpler test target than near-shore marine environments, where large aerosols complicate atmospheric correction and sediment runoff and high chlorophyll levels make interpretation of he data difficult. Calibrated data from both runoff and high chlorophyll levels make interpretation of the data difficult. Calibrated data from both sensors was provided in physical units of radiance. The atmospheric radiative transfer code, MODTRAN was used to remove the path radiance between the ground and sensor and the skylight reflected from the water surface. The resulting water-leaving spectrometer, and with values calculated form in-water properties using the HYDROLIGHT radiative transfer code. The agreement of the water-leaving radiance for the HYDICE data, the ground-truth spectral measurements, and the results of the radiative transfer code are excellent for wavelengths greater than 0.45 micrometers . The AVIRIS flight took place more than an hour closer to noon, which makes the radiance measurements not directly comparable. Comparisons to radiative transfer output for this later time indicate that the AVIRIS data is strongly by sun glint. Because water-leaving radiance is dependent upon the characteristics of the water, it can be analyzed for some of those properties. Using the CZCS algorithm based on the water-leaving radiance at two wavelengths, the chlorophyll content of Lake Tahoe was computed from the HYDICE and ground-truth data. Resulting values are slightly higher than measurements made two weeks earlier from water samples, indicating a growth in the phytoplankton population which is very plausible given the intervening atmospheric conditions. The success in determining water-leaving radiance and interpreting it for pigment concentration are very positive results for this early HYDICE flight. The interpretations made so far do not make use of the full spectral content of the data, so much room for advancement remains.

W.J. Rhea

Papers

A profiling optics and water return system for validation and calibration of ocean color imagery

Airborne hyperspectral imaging of cyanobacteria accumulations in the Potomac River

Airborne Remote Sensing of Surface Velocities in a Tidal River

Comparison of remote sensing algorithms for retrieval of suspended particulate matter concentration from reflectance in coastal waters

HYDICE data from Lake Tahoe: comparison to coincident AVIRIS and in-situ measurements