Our aim is to develop general methods  which may improve wide variety of Hyperspectral Remote Sensing information extraction tasks. In this field there are fundamental problems concerning understanding the interaction of solar energy with natural terrain surfaces: variations in incoming flux densities, Bidirectional Reflectance Distribution Function (BRDF) effects, roughness effects, and surface heterogeneity effects. New signal and spatial processing  concepts are offered leading the development of  algorithms allowing  identification of consistent relationships within the signal despite variations in acquisition effects and  spatial regions which are more informative than others.   The assessment of these algorithms is conducted in wide variety of  environmental remote sensing applications: precision agriculture, natural vegetation mapping, soil erosion estimation, soil mapping and atmospheric monitoring.