Within urban environments and other complex illumination conditions, tilted target surface or partial sky occlusions from nearby raised objects can have a significant impact on a target’s radiance observed from a spectral sensor. At the pixel level, these terms can be predicted and corrected for by modeling the impact collocated height data has on the scene radiometry. After properly accounting for these impacts, a Lambertian material’s retrieved spectral reflectivity should be the same at any location or orientation within the scene. This paper proposes a novel approach for using this constraint to iterate on atmospheric aerosol parameters until the difference of retrieved spectral reflectance of two pixels of the same material, but under different illumination conditions, is minimized.
The ARTEMIS hyperspectral sensor will be the first spaceborne hyperspectral sensor with an on-board real-time
processing capability. The ARTEMIS real-time processor utilizes both anomaly and material detection algorithms to
locate materials of potential interest. To satisfy the real-time processing timelines, the collected data must be reduced
from hundreds of bands to around 64 bins, where a bin can be a single band or the average of a set of bands. A signature
optimization study was conducted to compare various binning algorithms through the analysis of both the detection
characteristics and the discrimination performance before and after spectral binning.
Spectral registration errors occur in hyperspectral (HS) data when the reported channel center wavelengths accompanying a data cube (commonly called the wavefile) are inaccurate. Poor spectral registration can lead to errors in water vapor retrievals and in the correction of other atmospheric gases. This, in turn, leads to erroneous overall atmospheric correction of HS data, and reduced exploitation performance. We have developed a method to detect poor spectral registration using major atmospheric spectral features. The spectral features we use are the Fraunhofer "G" line at 430 nm, to O<sub>2</sub> absorption lines at 762 nm and 1268 nm, three water vapor absorption bands at 817 nm, 935 nm, and 1135 nm, and a CO<sub>2</sub> absorption line at 2055 nm. We check the alignment of the average, uncorrected background features with MODTRAN-modeled spectral radiance data. We will present our approach to spectral registration and the wavefile correction method we developed, based on the accurate channel center wavelengths determined for the various atmospheric features. We will also present results from various sensor types.
The performance of hyperspectral exploitation algorithms depends on the quality of the hyperspectral data processed. Some algorithms may perform better or be better suited to certain types or quality of data than other algorithms. To improve the hyperspectral exploitation production process, the dependencies of different types of algorithms to the quality of the hyperspectral data needs to be understood. A framework for predicting algorithm performance based on data parameters and metrics is presented. Figures of merit are defined for classes of algorithms which can be used to select between different algorithms to process a particular dataset. A training set of data is used to determine the dependence of each algorithm being tested in the class. Multiple regression is then applied to determine the dependence of the algorithm results on the different parameters and metrics. The performance on datasets not in the training set can then be predicted using the results of the regression analysis. Analysis of the regression results provides insight into the dependence of different types of algorithms on parameters of the data. In addition, the results provide insight into the data quality needed to provide quality exploitation products that meet minimum requirements. The technique is presented along with preliminary results for some basic algorithms in the atmospheric compensation and material identification categories.
A combined radiative transfer model and statistical approach to atmospheric correction of hyperspectral data, called MODFULL, has been developed and tested. MODFULL retrieves in-scene water vapor and aerosol (without the aid of dark pixels) which it uses for atmospheric correction. It also provides the user with checks on spectral registration of the hyperspectral data being processed. A description of this model will be given along with an analysis of its strengths and weaknesses. A summery of test results using comparisons with an industry standard Empirical Line Method (ELM) correction will also be presented.