发表机构
The College of Information Science and Technology, Beijing University of Chemical Technology; The School of Information Science and Engineering, Yunnan University; The Department of Signal Theory and Communications, Polytechnic University of Catalonia; The School of Information and Cyberspace Security, Ningxia University（北京化工大学信息科学与技术学院; 云南大学信息科学与工程学院; 加泰罗尼亚理工大学信号理论与通信系; 宁夏大学信息与网络空间安全学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对PolSAR图像分类中现有复值网络的不足，提出CV-SSMNet，通过构建复值状态空间模型、编码散射先验等方法，实现从局部散射结构到全局空间上下文的物理引导表示学习，实验验证了其有效性。
AI 中文摘要
极化合成孔径雷达（PolSAR）图像分类是物理感知地理人工智能的典型任务，其中土地覆盖语义与电磁散射机制紧密相关。许多现有复值网络能保留幅度相位信息，但在长距离空间依赖性建模方面受限，且仅将极化先验作为输入级或浅层辅助特征。本文提出CV-SSMNet，一种用于PolSAR图像分类的具有散射感知特征调制的物理感知复值状态空间网络。该方法在原始复域构建复值状态空间模型以捕获长距离空间依赖性并保留极化幅度相位耦合，同时将七个物理意义明确的散射先验编码为FiLM风格调制信号以在特征演化时自适应重新校准复值表示。实验表明CV-SSMNet具有竞争力的准确率、改进的区域一致性和更好的边界保留效果，支持将极化散射机制嵌入复值长距离地理人工智能表示学习的有效性。
英文摘要
Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are closely coupled with electromagnetic scattering mechanisms. Many existing complex-valued networks can preserve amplitude-phase information, but they are often limited in long-range spatial dependency modeling and usually incorporate polarimetric priors only as input-level or shallow auxiliary features. As a result, physical knowledge is insufficiently used to guide deep feature evolution. To address this issue, this paper proposes CV-SSMNet, a physics-aware complex-valued state-space network with scattering-aware feature modulation for PolSAR image classification. The proposed method builds a complex-valued state-space model (CV-SSM) in the original complex domain to capture long-range spatial dependencies while preserving polarimetric amplitude-phase coupling. Meanwhile, seven physically meaningful scattering priors, are encoded as FiLM-style modulation signals to adaptively recalibrate complex-valued representations during feature evolution. CV-SSMNet further integrates multi-scale complex convolutions, branch-wise CV-SSM encoding, prior-guided recalibration, and lightweight global context aggregation, enabling physically guided representation learning from local scattering structures to global spatial context. Experiments on three L-band benchmark datasets and an additional P-band BIOMASS evaluation demonstrate that CV-SSMNet achieves competitive accuracy, improved regional consistency, and better boundary preservation, supporting the effectiveness of embedding polarimetric scattering mechanisms into complex-valued long-range GeoAI representation learning.
Comments20pages, 14 figures, 10 tables
