Mapping spatial variations of iron oxide by-product minerals from EO-1 Hyperion

This study aimed to map mine waste piles and iron oxide by-product minerals from an Earth Observing 1 (EO-1) Hyperion data set that covers an abandoned mine in southwest Spain. This was achieved by a procedure involving data pre-processing, atmospheric calibration, data post-processing, and image classification. In several steps, the noise and artefacts in the spectral and spatial domains of the EO-1 Hyperion data set were removed. These steps include the following: (1) angular shift, which was used to translate time sequential data into a spatial domain; (2) along-track de-striping to remove the vertical stripes from the data set; and (3) reducing the cross-track low-frequency spectral effect (smile). The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm in combination with the radiance transfer code MODTRAN4 was applied for quantification and removal of the atmospheric affect and retrieval of the surface reflectance. The data set was post-processed (filtering, spectral polishing) in order to remove the negative values and noise that were produced as the a result of de-striping and atmospheric calibration. The Mahalanobis distance algorithm is used to differentiate the area covered by mine piles from other main land-use classes. The spatial variations of iron oxide and carbonate minerals within the mine area were mapped using the Spectral Feature Fitting (SFF) algorithm. The pre-processing of the data and atmospheric correction were vital and played a major role on the quality of the final output. The results indicate that the vertical stripes can be removed rather well by the local algorithm compared to the global method and that the FLAASH algorithm for atmospheric correction produces better results than the empirical line algorithm. The results also showed that the method developed for correcting angular shifts has the advantage of keeping the original pixel values since it does not require re-sampling. The classification results showed that the mine waste deposits can be easily mapped using available standard algorithms such as Mahalanobis Distance. The results obtained from the SFF method suggest that there is an abundance of different minerals such as alunite, copiapite, ferrihydrite, goethite, jarosite, and gypsum within the mine area. From a total number of 754 pixels that cover the mine area, 43 pixels were classified as sulphide and carbonate minerals and 711 pixels remained unclassified, showing no abundance of any dominant mineral within the area presented by these pixels.