Unbiased Validation of Hyperspectral Unmixing Algorithms

Hyperspectral unmixing is one of the most challenging tasks in the analysis of such data. There have been an array of algorithms proposed for this problem so far, but they are virtually always verified using random sampling, where training and test examples are drawn from the same image. Since such samples are spatially correlated and may be positioned close to each other, random sampling can induce the training-test information leak in the techniques that exploit spatial information during the unmixing process. We want to raise the attention of the community about this validation flaw in the context hyperspectral unmixing. We introduce the algorithm for unbiased validation of the unmixing techniques through splitting hyperspectral images into training and test samples that do not suffer from the training-test information leak. The experiments showed that the widely-used random sampling verification strategy leads to overly optimistic conclusions concerning the algorithm’s performance. This problem was mitigated with the proposed approach which allows us to rigorously validate unmixing techniques.