Hyperspectral image data encapsulates a wealth of information. Remote sensing using hyperspectral instruments allows collection of the spectral signature of ground objects over large regions. These spectral signatures can be used to determine the chemical composition of the objects and thus allow applications such as mineral detection and vegetation health monitoring from airborne or spaceborne platforms. One of the greatest impediments to widespread use of hyperspectral remote sensing data is that there is no quick and easy method for potential users of hyperspectral data to locate and access suitable datasets. The datasets are large, and data providers do not generally advertise their products. As such, the user community has remained static, consisting of a small set of knowledgeable users.
The traditional approach for managing and advertising large holdings of remote sensing image data has been to use a cataloging system that maintains text metadata about each image in the archive together with a “quick-look” browse image. The browse image is typically a heavily subsampled and compressed version of the original image (in black and white or color). By performing a spatial or temporal search of the metadata within the catalog, potential users of the data can effectively discover potential datasets of interest. By then visualizing the corresponding browse images, users can evaluate each of the potential datasets to determine whether the imagery is of use in their application. The key to the success of this paradigm is that the metadata supports discovery of potential datasets, whereas the spatially subsampled browse images support quick evaluation of the spatial quality of the imagery. It is argued here that, until now, no such discovery and evaluation paradigm has existed for hyperspectral data archives.
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