Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2409.01236v2
• Date: Tue, 29 Oct 2024 10:16:54 GMT
• Title: Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification
• Authors: Kangdao Liu, Tianhao Sun, Hao Zeng, Yongshan Zhang, Chi-Man Pun, Chi-Man Vong,
• Abstract summary: Hyperspectral image (HSI) classification involves assigning unique labels to each pixel to identify various land cover categories. Deep classifiers have achieved high predictive accuracy in this field, but they lack the ability to quantify confidence in their predictions. We introduce Spatial-Aware Conformal Prediction (textttSACP), a conformal prediction framework specifically designed for HSI data.
• Score: 39.71307720326761
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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. We then propose a conformal procedure that equips any trained HSI classifier with trustworthy prediction sets, ensuring that these sets include the true labels with a user-specified probability (e.g., 95\%). Building on this foundation, we introduce Spatial-Aware Conformal Prediction (\texttt{SACP}), a conformal prediction framework specifically designed for HSI data. This method integrates essential spatial information inherent in HSIs by aggregating the non-conformity scores of pixels with high spatial correlation, which effectively enhances the efficiency of prediction sets. Both theoretical and empirical results validate the effectiveness of our proposed approach. The source code is available at \url{https://github.com/J4ckLiu/SACP}.

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