Hyperspectral imagery has the capability of capturing spectral features of interest that can be used to differentiate among similar materials. While hyperspectral imaging has been demonstrated to provide data that enable classification of relatively broad categories, there remain open questions as to how fine of discrimination is possible. An application of this fine discrimination question is the potential that spectral features exist in the surface reflectance of ordinary civilian vehicles that would enable tracking of a particular vehicle across repeated hyperspectral images in a cluttered urban area.To begin to explore this question a vehicle tracking experiment was conducted in the summer of 2005 on the Rochester Institute of Technology (RIT) campus in Rochester, New York. Several volunteer vehicles were moved around campus at specific times coordinated with over flights of RIT's airborne Modular Imaging Spectrometer Instrument (MISI). MISI collected sequential images of the campus in 70 spectral channels from 0.4 to 1.0 microns with a ground resolution of approximately 2.5 meters. Ground truth spectra and photographs were collected for the vehicles.These data are being analyzed to determine the ability to uniquely associate a vehicle in one image with its location in a subsequent image. Initial results have demonstrated that the spectral measurement of a specific vehicle can be used to find the same vehicle in a subsequent image, although this is not always possible and is very dependent upon the specifics of the situation. Additionally, efforts are presented that explore predicted performance for variations in scene and sensor parameters through an analytical performance prediction model.

Spectral signature databases abound in the field of remote sensing. Scientists use these databases to assist in their analysis everyday. Many decisions are made about hyperspectral data and the observations made with this data based on the assumption that these databases contain "ground truth" representations of the signatures for materials sensed. For the most part, this is true if the team collecting the signatures that populate these databases follow sound practices when collecting this data. The data does, however, represent a very specific picture of the "truth". Signatures found in databases represent a specific collection configuration or geometry. The source of illumination, whether it is artificial or natural, is in a very specific location as is the sensor used to collect radiance for the derivation of the reflectance signatures. A signature found in the database is useful for only a very specific scenario, one that matches the geometry used during ground truth collection. There are other very significant factors regarding illumination field and scattering properties of the material and reference standards that influence the computed reflectance signature. This work will illustrate some of the dramatic variation that can exist in the reflectance signatures derived for the same material using different techniques. Difference upward of 30% may exist for the same material. These observations are presented so that scientists who look to these databases in the future will consider very carefully the metadata that is presented with the signatures that they use to make sure they are applicable to the phenomenology and collection scenario that they have under study. These observations should also point out that signatures presented without detailed metadata could be very hazardous to use if the outcome of the analysis being performed relies upon the absolute reflectance spectra being known.

This paper describes a collaborative collection campaign to spectrally image and measure a well characterized scene for hyperspectral algorithm development and validation/verification of scene simulation models (DIRSIG). The RIT Megascene, located in the northeast corner of Monroe County near Rochester, New York, has been modeled and characterized under the DIRSIG environment and has been simulated for various hyperspectral and multispectral systems (e.g., HYDICE, LANDSAT, etc.). Until recently, most of the electro-optical imagery of this area has been limited to very high altitude airborne or orbital platforms with low spatial resolutions. Megacollect 2004 addresses this shortcoming by bringing together, in June of 2004, a suite of airborne sensors to image this area in the VNIR, SWIR, MWIR, and LWIR regions. These include the COMPASS (hyperspectral VNIR,SWIR), SEBASS (hyperspectral LWIR), WASP (broadband VIS, SWIR, MWIR, LWIR) and MISI (hyperspectral VNIR, broadband SWIR, MWIR, LWIR). In conjunction with the airborne collections, an extensive ground truth measurement campaign was conducted to characterize atmospheric parameters, select targets, and backgrounds in the field. Laboratory measurements were also made on samples to confirm the field measurements. These spectral measurements spanned the visible and thermal region from 0.4 to 20 microns. These measurements will help identify imaging factors that affect algorithm robustness and areas of improvement in the physical modeling of scene/sensor phenomena. Reflectance panels have also been deployed as control targets to both quantify sensor characteristics and atmospheric effects. A subset of these targets have also been deployed as an independent test suite for target detection algorithms. Details of the planning, coordination, protocols, and execution of the campaign will be discussed with particular emphasis on the ground measurements. The system used to collect the metadata of ground truth measurements and disseminate this data will be described. Lastly, lessons learned in the field will be underscored to highlight additional measurements and changes in protocol to improve future collections of this area.