Granite and granodiorite identification using spectral unmixing techniques

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 determines the efficiency of data analysis. The importance of this issue is directly connected with the ever-increasing quantity of data provided by numerous airborne, field and laboratory operated sensors, with their synergistic use as well as with the accuracy of data processing algorithms and results verification. We present here some results from a study of different spectral unmixing techniques over two similar rock types such as granite and granodiorite in relation to objects type and proportions determination. Experimental data from field and laboratory spectral reflectance measurements in the visible and near infrared band are used. Various decomposition methods (linear unmixing, clustering) are applied and evaluated. Spectral linear unmixing is efficient approach to the spectral decomposition of multichannel remotely sensed data. A main problem to its process is that the number of spectral components (has to be correctly distinguished. Therefore, the evaluating of the possibility of using spectral mixture decomposition in relation to their type and proportion determination for subpixel identification is described.