Online Semisupervised Active Classification for Multiview PolSAR Data

Nie, Xiangli; Fan, Mingyu; Huang, Xiayuan; Yang, Wenjing; Zhang, Bo; Ma, Xiaoshuang

doi:10.1109/tcyb.2020.3026741

Cited by 13 publications

(1 citation statement)

References 46 publications

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“…Liu et al [45] proposed a controllable convex hull-based exemplar selection strategy to alleviate the catastrophic forgetting problem in incremental learning for scene classification in remote sensing images. Nie et al [46] addressed the problem of online incremental learning for multiview classification in polarimetric SAR (PolSAR) data. Their method integrates both the multiview co-regularization and graph regularization techniques, leveraging the disagreement among multiview predictors.…”

Section: B Incremental Learning In the Sar Domainmentioning

confidence: 99%

LIME-Assisted Automatic Target Recognition With SAR Images: Toward Incremental Learning and Explainability

Oveis,

Giusti,

Ghio

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Integrating an automatic target recognition (ATR) system into real-world applications presents a challenge as it may frequently encounter new samples from unseen classes. To overcome this challenge, it is necessary to adopt incremental learning, which enables the continuous acquisition of new knowledge while retaining previous knowledge. This paper introduces a novel, multi-purpose interpretability metric for ATR systems that employ synthetic aperture radar (SAR) images. The metric leverages the Local Interpretable Model-Agnostic Explanation (LIME) algorithm, enhancing human decision-making by providing a secondary measure alongside the conventional classification score. Additionally, the proposed metric is employed to analyze the robustness of Convolutional Neural Networks (CNNs) by examining the impact of target features and irrelevant background correlations on recognition results. Finally, we demonstrate the effectiveness of the proposed metric in the context of incremental learning. By utilizing the proposed interpretability metric, we select exemplars in an incremental learning scenario, resulting in improved performance and showcasing the application potential of our proposed methodology. The network is fine-tuned sequentially with unknown samples recognized by the Openmax classifier and exemplars from the old known classes, which are selected based on the proposed interpretability metric. The effectiveness of this approach is demonstrated using the publicly available MSTAR dataset.

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Section: B Incremental Learning In the Sar Domainmentioning

confidence: 99%