Supervised hyperspectral image classification with rejection

Hyperspectral image classification is a challenging classification problem: obtaining complete and representative training sets is costly; pixels can belong to unknown classes; and it is generally an ill-posed problem. The need to achieve high classification accuracy surpasses the need to classify the entire image. To achieve this, we use classification with rejection by providing the classifier an option not to classify a pixel and consequently reject it. We propose a method for supervised hyperspectral image classification combining the use of contextual priors with classification with rejection. Rejection is introduced as an extra class that models the probability of classifier failure. We validate the resulting algorithm in the AVIRIS Indian Pines scene and illustrate the performance increase resulting from classification with rejection.