Multiview Manifold Evidential Fusion for PolSAR Image Classification

• URL: http://arxiv.org/abs/2510.11171v1
• Date: Mon, 13 Oct 2025 09:05:51 GMT
• Title: Multiview Manifold Evidential Fusion for PolSAR Image Classification
• Authors: Junfei Shi, Haojia Zhang, Haiyan Jin, Junhuai Li, Xiaogang Song, Yuanfan Guo, Haonan Su, Weisi Lin,
• Abstract summary: We propose a new framework to integrate PolSAR manifold learning and evidence fusion into a unified architecture.<n>Experiments on three real-world PolSAR datasets demonstrate that the proposed method consistently outperforms existing approaches in accuracy, robustness, and interpretability.
• Score: 51.41332458376411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Polarimetric Synthetic Aperture Radar (PolSAR) covariance matrices and their extracted multi-features - such as scattering angle, entropy, texture, and boundary descriptors - provide complementary and physically interpretable information for image classification. Traditional fusion strategies typically concatenate these features or employ deep learning networks to combine them. However, the covariance matrices and multi-features, as two complementary views, lie on different manifolds with distinct geometric structures. Existing fusion methods also overlook the varying importance of different views and ignore uncertainty, often leading to unreliable predictions. To address these issues, we propose a Multiview Manifold Evidential Fusion (MMEFnet) method to effectively fuse these two views. It gives a new framework to integrate PolSAR manifold learning and evidence fusion into a unified architecture. Specifically, covariance matrices are represented on the Hermitian Positive Definite (HPD) manifold, while multi-features are modeled on the Grassmann manifold. Two different kernel metric learning networks are constructed to learn their manifold representations. Subsequently, a trusted multiview evidence fusion, replacing the conventional softmax classifier, estimates belief mass and quantifies the uncertainty of each view from the learned deep features. Finally, a Dempster-Shafer theory-based fusion strategy combines evidence, enabling a more reliable and interpretable classification. Extensive experiments on three real-world PolSAR datasets demonstrate that the proposed method consistently outperforms existing approaches in accuracy, robustness, and interpretability.

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