Spectral Signatures

This paper presents a new method for automatically determining the optimum regression model, which enable the estimation of a parameter. The concept lies on the combination of k spectral pre-processing algorithms (SPPAs) that enhance spectral features correlated to the desired parameter. Initially a pre-processing algorithm uses as input a single spectral signature and transforms it according to the SPPA function. A k-step combination of SPPAs uses k pre-processing algorithms serially. The result of each SPPA is used as input to the next SPPA, and so on until the k desired pre-processed signatures are reached. These signatures are then used as input to three different regression methods: the Normalized band Difference Regression (NDR), the Multiple Linear Regression (MLR) and the Partial Least Squares Regression (PLSR). Three Simple Genetic Algorithms (SGAs) are used, one for each regression method, for the selection of the optimum combination of k SPPAs. The performance of the SGAs is evaluated based on the RMS error of the regression models. The evaluation not only indicates the selection of the optimum SPPA combination but also the regression method that produces the optimum prediction model. The proposed method was applied on soil spectral measurements in order to predict Soil Organic Matter (SOM). In this study, the maximum value assigned to k was 3. PLSR yielded the highest accuracy while NDR’s accuracy was satisfactory compared to its complexity. MLR method showed severe drawbacks due to the presence of noise in terms of collinearity at the spectral bands. Most of the regression methods required a 3-step combination of SPPAs for achieving the highest performance. The selected pre-processing algorithms were different for each regression method since each regression method handles with a different way the explanatory variables.

An integrated analysis of recent satellite imageries and dated aerial photos demonstrated to be a good investigation tool (Gallo et al., 2009) for the identification of new sites and for the assessment of landscape changes of wide archaeological areas in Ethiopia.In the Melka Kunture archaeological sequence, the obsidian exploitation represents a leitmotiv during the last 1.7 Myr and the first known example of Oldowan utilization (Piperno et al., 2009).The primary nearest obsidian source, the site of Balchit, 7 km North from Melka Kunture, is a dome-flow dated to 4.37 ± 0.07 Myr (Chernet et al., 1998). Large areas of the Balchit site are covered by secondary obsidian debris resulting from the erosion of primary sources.The proximity between primary and secondary Balchit obsidian sources and the high quality of this raw material, easily available in large quantity, represent a unique condition in the framework of the Oldowan and Acheulian East African sites (Piperno et al., 2009).In order to evaluate the human impact on the multitemporal change of obsidian sources, the land use of the study area, following the CORINE Land Cover Nomenclature, has been classified from aerial photos and IKONOS II imageries, respectively dated to 1972 and 2006. The accurate positioning of the primary and secondary sources and their extent have been measured thanks to the multispectral characteristics and to the high spatial resolution of the available imageries.Satellite scenes, covering an area of about 100 km2, have been also utilized in stereoscopy for the creation of the new topographic map, at the scale of 1:10,000, and of the Digital Elevation Model (DEM).Images orientation has been performed through the use of Rational Polynomial Coefficients (RPC) which accuracy has been improved by the availability of Ground Control Points (GCPs) properly measured during a DGPS survey. Then, the images have been orthorectified and radiometrically and spectrally enhanced in order to favour the recognition of obsidian presence, in respect with ground observations collected during fieldworks.Photointerpretation and semi-automatic classification processes of images have been performed with the support of spectral signatures of obsidian samples recorded by a FieldSpec Pro spectroradiometer, ranging in the visible-short wave infrared electromagnetic interval (0.4–2.5 μm).