Multiple instance learning for hyperspectral image analysis

Multiple instance learning is a recently researched learning paradigm that allows a machine learning algorithm to learn target concepts with uncertainty in the class labels of training data. In the following, this approach is assessed for use in hyperspectral image analysis. Two leading MIL algorithms are used in a classification experiment and results are compared to a state-of-the-art context-based classifier. Results indicate that using a MIL based approach may improve learned target models and subsequently classification results.