A multi-modal (hyperspectral, multispectral, and LIDAR) imaging data collection campaign was conducted just south of Rochester New York in Avon, NY on September 20, 2012 by the Rochester Institute of Technology (RIT) in conjunction with SpecTIR, LLC, the Air Force Research Lab (AFRL), the Naval Research Lab (NRL), United Technologies Aerospace Systems (UTAS) and MITRE. The campaign was a follow on from the SpecTIR Hyperspectral Airborne Rochester Experiment (SHARE) from 2010. Data was collected in support of the eleven simultaneous experiments described here. The airborne imagery was collected over four different sites with hyperspectral, multispectral, and LIDAR sensors. The sites for data collection included Avon, NY, Conesus Lake, Hemlock Lake and forest, and a nearby quarry. Experiments included topics such as target unmixing, subpixel detection, material identification, impacts of illumination on materials, forest health, and in-water target detection. An extensive ground truthing effort was conducted in addition to collection of the airborne imagery. The ultimate goal of the data collection campaign is to provide the remote sensing community with a shareable resource to support future research. This paper details the experiments conducted and the data that was collected during this campaign.
Proc. SPIE. 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
KEYWORDS: Hyperspectral imaging, Detection and tracking algorithms, Sensors, Calibration, Reflectivity, Image analysis, Modulation transfer functions, Data conversion, Atmospheric sensing, Global Positioning System
Spectral unmixing is a type of hyperspectral imagery (HSI) sub-pixel analysis where the constituent spectra and abundances
within the pixel are identified. However, validating the results obtained from spectral unmixing is very difficult due to a lack
of real-world data and ground-truth information associated with these real-world images. Real HSI data is preferred for
validating spectral unmixing, but when there is no HSI truth-data available, then validation of spectral unmixing algorithms
relies on user-defined synthetic images which can be generated to exploit the benefits (or hide the flaws) in the new
unmixing approaches. Here we introduce a new dataset (SHARE 2012: large edge targets) for the validation of spectral
unmixing algorithms. The SHARE 2012 large edge targets are uniform 9m by 9m square regions of a single material (grass,
sand, black felt, or white TyVek). The spectral profile and the GPS of the corners of the materials were recorded so that the
heading of the edge separating any two materials can be determined from the imagery. An estimate for the abundance of two
neighboring materials along a common edge can be calculated geometrically by identifying the edge which spans multiple
pixels. These geometrically calculated abundances can then be used as validation of spectral unmixing algorithms. The
size, shape, and spectral profiles of these targets also make them useful for radiometric calibration, atmospheric adjacency
effects, and sensor MTF calculations. The imagery and ground-truth information are presented here.
Fractional abundance maps have been produced from Hyperion hyperspectral data over Oaxaca, Mexico, by applying a
new spatially adaptive spectral unmixing algorithm. The goal of this research is to produce land-use maps for aiding
archaeologists studying the Zapotec civilization. However, to correlate the fractional abundance maps generated from the
HSI image processing, a relationship between the known materials located in Oaxaca, Mexico, and the spectral profiles of
these materials must be established. A field campaign during December 2011, (the dry season in Oaxaca) took place for
the explicit task of obtaining spectral profiles of the most common materials found in the region. Ground-truth information
was collected for three Oaxaca valleys (Tlacolula, Yanhuitlan, and Ycuitla). Common materials and associated regions
were recorded and material samples were collected at many of these locations. Laboratory reflectance spectral profiles
of the collected material samples are measured after the field campaign using a FieldSpec Pro. Wavelength ranges of the
FieldSpec Pro spanned 350-2500nm matching that of the hyperspectral imagery collected from the Hyperion sensor on
board the EO-1 satellite. GIS maps of the three valleys in Oaxaca, Mexico, are used to identify where these samples were
collected and correspond to the laboratory measured material samples. The spectral library entries obtained correspond to
bare soils, senescent agricultural vegetation, senescent natural vegetation, and terra cotta tile.
Linear spectral unmixing and endmember selection are two of the many tasks that can be accomplished using hyperspectral
imagery. The quality of the unmixing results depends on an accurate estimate of the number of endmembers used in
the analysis. Too many estimated endmembers produce over fitting of the spectral unmixing results; too few estimated
endmembers produce spectral unmixing results with large residual errors. Several statistical and geometrical approaches
have been developed to estimate the number of endmembers, but many of these approaches rely on using the global
dataset. The global approach does not take into consideration local endmember variability, which is of particular interest
in high-spatial resolution imagery. Here, the number of endmembers within local image tiles is estimated by using a novel,
spatially adaptive approach. Each pixel is unmixed using the locally identified endmembers and global abundance maps
are generated by clustering these locally derived endmembers. Comparisons are made between this new approach and
an established global method that uses PCA to estimate the number of endmembers and SMACC to identify the spectra.
Multiple images with varying spatial resolution are used in the comparison of methodologies and conclusions are drawn
based on per-pixel residual unmixing errors.
The Sea-viewing Wide Field of View Sensor (SeaWiFS) was launched during the summer of 1997. While its primary purpose was to provide quantitative data on ocean bio-optical properties at a global scale, its bi-linear gain design allows it to provide data over land as well. Thus, there has been greater interest in understanding the radiometric calibration of the sensor for both gain levels. The Remote Sensing Group of the Optical Sciences Center at the University of Arizona has been using vicarious calibration techniques that rely on ground-based test sites to calibrate a variety of sensors since the mid-1980s. The results of applying these techniques to SeaWiFS are presented here. Three ground-reference data sets are presented, the first from White Sands Missile Range in October 1997, the second from Railroad Valley Playa, Nevada in June 1998, and the third from Railroad Valley Playa in April 2000. The technique used here is a modified version of the reflectance-based method. In this technique, results from ground-based measurements of the surface and atmosphere are used in a radiative transfer code to determine the calibration coefficients for SeaWiFS. The results for all three cases are compared with calibration coefficients derived from the onboard calibration and vicarious calibration approaches used for SeaWiFS as well as to results.