A Geostatistically Based Preprocessing Algorithm for Hyperspectral Data Analysis

We present a preprocessing algorithm for hyperspectral remote sensing datasets. The algorithm is based on a geostatistical method and should be helpful when a spatial relationship is detected in a dataset. One significant advantage of hyperspectral remote sensing using spectral profiles is the ability to compare an unknown pixel's profile with endmembers that have already been identified by a variety of methods (e.g., laboratory experiments with high-precision spectrometers), with the final goal of determining the unknown pixel. The conditions under which the airborne or spaceborne hyperspectral data are collected, however, differ from those that prevail in the laboratory or field. Therefore, a dataset should be preprocessed so as to eliminate or considerably reduce these differences; the algorithm presented here could be used for that purpose. The result will not only improve the smoothness of spectral profiles, but it may also offer advantages for geological investigations to study mineral anomalies using hyperspectral data. Concentrations of minerals in rock bodies often have certain patterns and follow trends that can be modeled by computing a semivariogram. The advantages of using such a trend have induced mining engineers to develop innovations in geostatistics. These trends should be taken into account when handling hyperspectral datasets. In all methods presented for boosting spectral profiles, the spatial relationships among pixels' DNs are neglected, but, in the method presented here, this relationship is calculated by geostatistics, and an algorithm is applied to improve spectral profiles. The nugget effect is calculated separately for each channel, and its square root is subtracted from the reflectance of all pixels in that channel. Finally, we examine the effectiveness and validation of the method examined using the AVIRIS dataset from Cuprite, Nevada. The results are satisfactory, as the algorithm yields a better mineral detection process.