Spectral Unmixing for Information Extraction

“From pixels to processes” – this the shortest and most essential formulation of all studies, experiments and investigations carried out in the field of Earth remote sensing observations. This formulation reveals two main directions of data interpretation: the first related to classification and feature retrieval, and the second associated with multi-temporal aspects of remotely sensed data and concerning change detection and processes tracking and modeling. State assessment, trends forecasting and predictions are the goals of the environmental and land cover monitoring. Further implementation of the investigation results resemble the decision making and problem-solving nature of remotely sensed data outputs. The development of efficient technologies for data analysis is one of the most challenging issues that the remote sensing community is facing. Matters of data reduction, processing algorithms accuracy, information amount, cost and time saving determine the efficiency of data analysis. The importance of this issue is directly connected with the ever-increasing quantity of data provided by numerous optical, thermal and microwave sensors, with their synergistic use as well as with the accuracy of data processing algorithms and results verification. With all this in mind we present here some results from a study of different spectral unmixing techniques over rock-soil-vegetation objects in relation to mixtures decomposition, objects type and proportions determination and biophysical properties retrieval. Experimental data from field and laboratory spectral reflectance measurements in the visible and near infrared band have been used, various decomposition methods (linear unmixing, clustering, colorimetric analyses, etc.) have been applied and evaluated, comparison between empirical and simulation models has been performed. * Corresponding author.