Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification involves assigning unique labels to each pixel to identify various land cover categories. While deep classifiers have achieved high predictive accuracy in this field, they lack the ability to rigorously quantify confidence in their predictions. This limitation restricts their application in critical contexts where the cost of prediction errors is significant, as quantifying the uncertainty of model predictions is crucial for the safe deployment of predictive models. To address this limitation, a rigorous theoretical proof is presented first, which demonstrates the validity of Conformal Prediction, an emerging uncertainty quantification technique, in the context of HSI classification. Building on this foundation, a conformal procedure is designed to equip any pre-trained HSI classifier with trustworthy prediction sets, ensuring that the true labels are included with a user-defined probability (e.g., 95%). Furthermore, a novel framework of Conformal Prediction specifically designed for HSI data, called Spatial-Aware Conformal Prediction ( SACP ), is proposed. This framework integrates essential spatial information of HSI by aggregating the non-conformity scores of pixels with high spatial correlation, effectively improving the statistical efficiency of prediction sets. Both theoretical and empirical results validate the effectiveness of the proposed approaches. The source code is available at https://github.com/J4ckLiu/SACP