Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification

Understand

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.
• Quantifying the certainty of model predictions is crucial for the safe usage of predictive models, and this limitation restricts their application in critical contexts where the cost of prediction errors is significant.
• To support the safe deployment of HSI classifiers, we first provide a theoretical proof establishing the validity of the emerging uncertainty quantification technique, conformal prediction, in the context of HSI classification.