Mitigating SAR Out-of-distribution Overconfidence based on Evidential Uncertainty
Zhou, Xiaoyan; Tang, Tao; Sun, Zhongzhen; Kuang, Gangyao; Heikkilä, Janne; Liu, Li (2024-08-14)
IEEE
14.08.2024
X. Zhou, T. Tang, Z. Sun, G. Kuang, J. Heikkilä and L. Liu, "Mitigating SAR Out-of-distribution Overconfidence based on Evidential Uncertainty," in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2024.3443330.
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202409035701
https://urn.fi/URN:NBN:fi:oulu-202409035701
Tiivistelmä
Abstract
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is extensively applied in both military and civilian sectors. Nevertheless, test and training data distribution may differ in the open world. Therefore, SAR Out-of-Distribution (OOD) detection is important because it enhances the reliability and adaptability of SAR systems. However, most OOD detection models are based on maximum likelihood estimation and overlook the impact of data uncertainty, leading to overconfidence output for both in-distribution (ID) and OOD data. To address this issue, we consider the effect of data uncertainty on prediction probabilities, treating these probabilities as random variables and modeling them using Dirichlet distribution. Building on this, we propose an Evidential Uncertainty aware Mean Squared Error (UMSE) loss function to guide the model in learning highly distinguishable output between ID and OOD data. Furthermore, to comprehensively evaluate OOD detection performance, we have compiled and organized some publicly available data and constructed a new SAR OOD detection dataset named SAR OOD. Experimental results on SAR-OOD demonstrate that the UMSE approach achieves state-of-the-art performance. The code and data are available at: https://github.com/Xiaoyan-Zhou/UMSE-SAR-OOD-Detection.
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is extensively applied in both military and civilian sectors. Nevertheless, test and training data distribution may differ in the open world. Therefore, SAR Out-of-Distribution (OOD) detection is important because it enhances the reliability and adaptability of SAR systems. However, most OOD detection models are based on maximum likelihood estimation and overlook the impact of data uncertainty, leading to overconfidence output for both in-distribution (ID) and OOD data. To address this issue, we consider the effect of data uncertainty on prediction probabilities, treating these probabilities as random variables and modeling them using Dirichlet distribution. Building on this, we propose an Evidential Uncertainty aware Mean Squared Error (UMSE) loss function to guide the model in learning highly distinguishable output between ID and OOD data. Furthermore, to comprehensively evaluate OOD detection performance, we have compiled and organized some publicly available data and constructed a new SAR OOD detection dataset named SAR OOD. Experimental results on SAR-OOD demonstrate that the UMSE approach achieves state-of-the-art performance. The code and data are available at: https://github.com/Xiaoyan-Zhou/UMSE-SAR-OOD-Detection.
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