Generic Evaluation Metrics for Hyperspectral Data Unmixing. Issue 1 (2nd January 2016) - Bchir, Ouiem; Ben Ismail, Mohamed Maher
Abstract: We propose novel generic performance metric for hyperspectral unmixing techniques. This relative metric compares two abundance matrices. The first one represents the unmixing result. The second matrix can be either another unmixing result or the ground truth of the hyperspectral scene. This metric starts by computing coincidence matrices corresponding to the two abundance matrices, then the comparison is carried out by computing statistics of the number of pairs of data points that have high abundances with respect to the same endmember for the first unmixing approach, but have large abundance differences with respect to the same endmember for the second unmixing technique, or large differences in both. The main advantage of this metric approach is that there is no need to pair the endmembers of the two unmixing approaches. Rather, it assumes that the pixels, which are considered as different/same material by one unmixing approach should also be considered different/same material by the other. Our initial experiments on synthetic dataset have indicated the appropriateness of the proposed performance measures to assess unmixing techniques. Finally, the proposed metric are assessed using real dataset, and existing hyperspectral unmixing techniques.