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Images collected by remote sensors placed on airplanes or satellites are useful in many different ways, but they can trick sometimes if errors cannot be screened.

This was a concern to Filipe Condessa, co-author of the paper "Supervised Hyperspectral Image Classification with Rejection”. It received the “Best Student Paper Award” in the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). The paper introduces a framework for robust image classification of remote sensing aerial images. The goal is to classify the image components according to their composition (for example to separate wheat fields from soy fields). This is a way to render the procedure robust to sensor noise and to human errors in the training examples, by considering the option to reject parts of the classification (to abstain from erroneous classifications) and by using contextual cues.

The improved classification performance was not indifferent to Geoscience and Remote Sensing community, among the 99 submitted full papers, and left Filipe Condessa along with his two advisors, José Bioucas-Dias (IST) and Jelena Kovacévič (CMU), happy and proud.