QUANTITATIVE SUBPIXEL SPECTRAL DETECTION OF TARGETS IN MULTISPECTRAL IMAGES

Spectral mixture analysis was used to determine threshold detection limits of target materials in the presence of background materials within the field of view under various simulated but realistic compositional, instrumental, and topographical conditions. Detection thresholds were determined for the cases where the target is detected as (1) a component of a spectral mixture (continuum threshold analysis) and (2) residuals (residual threshold analysis). In continuum threshold analysis, the target was included as a component during unmixing thereby permitting evaluation of target detectability. In residual threshold analysis, the unmodeled target was detected as wavelength-dependent deviations of the spectral mixture (target included) from the predicted spectrum (mixtures of the modeled background spectra). High resolution laboratory spectra were used to test the ''best case'' for target detection in spectral mixtures. Data quality was then decreased to simulate the effects of various imaging instruments (spectral sampling and noise) and changes in lighting geometry. The results show that the contrast of the target spectrum, relative to mixtures of background spectra, determines which level of spectral mixture analysis (continuum or residual analysis) detects the target at the lower threshold. Continuum analysis provides lower thresholds when there is contrast between the target and mixtures of the background spectra throughout much of the spectrum, whereas residual analysis improves detectability when the uniqueness of the target is found in narrow absorption feature(s). Varying spectral sampling (by wavelength) changes the contrast (and detectability) of the target and background materials. Although simple examples of target and background materials are used in this paper, they illustrate a general approach for evaluating the spectral detectability of terrestrial and planetary targets at the subpixel scale. Using spectral mixing analysis as a framework makes it possible to specify the conditions that are necessary to detect a particular material and the most appropriate imaging system for a given application.