The quantitative evaluation of algorithms applied to remotely sensed hyperspectral imagery require data sets with known ground truth. A recent data collection known as SHARE 2012, conducted by scientists in the Digital Imaging and Remote Sensing Laboratory at the Rochester Institute of Technology together with several outside collaborators, acquired hyperspectral data with this goal in mind. Several experiments were designed, deployed, and ground truth collected to support algorithm evaluation. In this paper, we describe two experiments that addressed the particular needs for the evaluation of subpixel detection and unmixing algorithms. The subpixel detection experiment involved the deployment of dozens of nearly identical subpixel targets in a random spatial array. The subpixel targets were pieces of wood painted either green or yellow. They were sized to occupy about 5% to 20% of the 1 m pixels. The unmixing experiment used novel targets with prescribed fractions of different materials based on a geometric arrangement of subpixel patterns. These targets were made up of different fabrics with various colors. Whole pixel swatches of the same materials were also deployed in the scene to provide in-scene endmembers. Alternatively, researchers can use the unmixing targets alone to derive endmembers from the mixed pixels. Field reflectance spectra were collected for all targets and adjacent background areas. While efforts are just now underway to evaluate the detection performance using the subpixel targets, initial results for the unmixing targets have demonstrated retrieved fractions that are close approximations to the geometric fractions. These data, together with the ground truth, are planned to be made available to the remote sensing research community for evaluation and development of detection and unmixing algorithms.
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.
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