Mineral mapping and vegetation removal via data-calibrated pixel unmixing, using multispectral images

Abstract

In this paper an image unmixing process is described which is based on the assumption that pixel reflectance is a linear mix of component reflectances. Three stages are used initially to calibrate the multispectral data to shaded reflectance: (I) minimum value subtraction, (2) band-mean standardization and (3) reflectance-mean equalization. Using laboratory or field spectra, linear mixing equations are then solved for substance proportions. For a geological test site in North Queensland, Australia, two vegetation types (green and dry) and four mineral types (clay, haematite, goethite and quartz) were unmixed using NSOOI aircraft scanner data. The six images created show the spatial abundance of individual vegetation and mineral types. The obscuring eITect of both green and dry vegetation was removed from mineral maps by separately scaling up mineral proportions in each pixel. Unmixed mineral maps were used to locate potentially economic hydrothermal alteration zones. In addition, scaled mineral proportions were used to recalculate brightness values for the original wavelength bands producing de-vegetated geological reflectance images which are useful for identification of rock· types.

Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain

“…Where the effects of vegetation prevail, image processing techniques such as principal component analysis (Fraser & Green, 1987;Loughlin, 1991) and spectral unmixing (Bierwirth, 1990;Chabrillat et al, 2000;Zhang et al, 2005) have been employed to try and separate the spectral responses of vegetation and substrate, and to detect rock exposures at sub-pixel resolutions. Alternatively, indirect lithological discrimination is possible if geobotanical relationships with the underlying substrates are realised (Paradella et al, 1997;Leverington, 2010).…”

The Impact of Vegetation on Lithological Mapping Using Airborne Multispectral Data: A Case Study for the North Troodos Region, Cyprus

“…Despite this, the impact of dry grass in the imagery seems to be slightly greater than predicted, with obscuring of the lithologies occurring for 15% less fractional cover, on average, than their theoretical responses. Such relatively minor deviations could be due to noise in the data [26], or due the influence of non-dry grass vegetation on both the ATM B8/B9 band ratio and SAVI. Although spectral unmixing of the ATM imagery could potentially be used to extract accurate sub-pixel abundances of each specific vegetation type, rigorous application of this technique was hindered by the lack of representative spectral end-members for some of the minor scene constituents (e.g., roads, mine spoil heap, buildings, trees).…”

“…A number of techniques have been developed to try to overcome the effects of vegetation on spectral discrimination of the underlying lithological substrate. These include techniques based on Principal Component Analysis [20], statistical "forced invariance" [25], spectral unmixing [26][27][28][29][30][31], use of ancillary data [32][33][34] and correction of diagnostic absorption depths using linear regression models [35,36]. Despite the significant body of research on overcoming the problems posed by vegetation cover, little attention has been paid to demonstrating how the effects observed through spectral mixing analysis translate to remotely sensed imagery.…”

A New Method for Accuracy Assessment of Sub-Pixel Classification Results

“…457 The grey value of such mixels is a composition of the radiometric properties of the several classes (objects) and therefore generates some confusion in classification procedures [14,15] . Traditional classifiers like MLC assign each pixel to only one class.…”