These pages describe a project to investigate techniques for identifying substances from their spectral signatures. The work is being performed by Dr. Christine Clark, with occasional hinderance :-) from Dr. Adrian F. Clark. The work was funded by EPSRC grant number GR/K23584 and stems from previous work carried out by Christine in association with Dr. Tony Cañas of Imperial College. Subsequent to the EPSRC-funded research, work has been supported by industry. These pages include information from both the current project and the previous one.
Multi-spectral imagery (sometimes called hyperspectral imagery) plays a significant role in Earth resource survey and evaluation and has been an essential part of terrestrial and planetary exploration. The capabilities of instruments in resolution and spectra discrimination are constantly being improved upon to meet the increasingly expanded requirements in the fields of geophysical survey, mineral/petrological exploration, land and water use and management, among many others.
Multi-spectral imaging sensors are now routinely in use. Well-known examples are GER, which images in 63 wavebands, and the Airborne Visible/Infrared Imaging spectrometer (AVIRIS), which images in 224 wavebands in the range 0.4-2.45 microns, giving 224 images, each of 614 x 512 pixels. With this increasing utilization of imaging spectrometer data, automatic identification of spectral signatures emanating from this imagery would be an invaluable facility as a precursor to classifying each pixel. Existing methods for identifying constituent spectra typically rely on spectra that are selected either manually or involve manual intervention. The aim of our research work is to assess the viability of linear and non-linear approaches for spectral recognition and to devise techniques suitable for fully-automatic analysis.
Further information is available on:
Last updated 2012-06-21 11:12:47 by Adrian F. Clark. [Accessibility information.]