Classic target decomposition methods use scattering space in their approaches. However, the goal for this project is to investigate whether a different approach to retrieve accurate and reliable estimates on the earth composition is possible when using the feature space with covariance matrix-based features. The approach consists of four steps. Generating multidimensional feature space data from sea ice scenes, extracting endmembers, finding the optimal number of endmembers in the scene and finding the contribution for the endmembers to each of the polarimetric feature pixels in the scene. In order to validate the performance of the approach several validation steps where conducted. Classification of the endmembers, calculating the average reconstruction error, classification of the scene and studding the abundance coefficients were some of these steps. Also, generation of synthetic data was conducted as an additional review of the approach. The system in this approach does not take in to account the variability of the polarimetric feature values in the different classes. It also assumes that the pixels are linearly mixed, something they probably not are. As a consequence, the approach is not able to retrieve accurate and reliable estimates on the earth composition for scenes consisting of sea ice. However, the approach gave good results on the synthetic datasets. Further work and investigation on the approach would include adapting the approach to consider the variability all sea ice data suffers from. Further, the methods considering linear mixing should then be replaced with methods considering nonlinear mixing.