ECP-Mamba：一种面向PolSAR图像分类的高效多尺度自监督对比学习方法与状态空间模型
ECP-Mamba: An Efficient Multi-scale Self-supervised Contrastive Learning Method with State Space Model for PolSAR Image Classification
AI总结：
本文提出ECP-Mamba，通过多尺度自监督对比学习缓解标注依赖，并利用定制螺旋扫描的Mamba状态空间模型提升PolSAR图像分类的计算效率与精度。
摘要：
近年来，极化合成孔径雷达（PolSAR）图像分类在深度神经网络的推动下取得了显著进展。然而，当前基于深度学习的PolSAR分类方法面临困难，原因在于其对大量标注数据的依赖以及Transformer等架构的计算效率低下。本文提出ECP-Mamba，这是一个将多尺度自监督对比学习与状态空间模型（SSM）骨干网络相结合的高效框架。具体而言，ECP-Mamba通过基于局部到全局特征对应的多尺度预测预训练任务来解决标注稀缺问题，该任务采用简化的自蒸馏范式，无需负样本对。为提高计算效率，首次将Mamba架构（一种选择性SSM）针对逐像素PolSAR分类任务进行定制，设计了螺旋扫描策略。该策略优先考虑中心像素附近具有因果相关性的特征，充分利用逐像素分类任务的局部化特性。此外，提出了轻量级Cross Mamba模块，以最小的开销促进互补的多尺度特征交互。在四个基准数据集上的大量实验证明了ECP-Mamba在平衡高精度与资源效率方面的有效性。在Flevoland 1989数据集上，ECP-Mamba取得了最先进的性能，总体精度为99.70%，平均精度为99.64%，Kappa系数为99.62e-2。我们的代码将在https://github.com/HaixiaBi1982/ECP_Mamba上公开。
英文摘要：
Recently, polarimetric synthetic aperture radar (PolSAR) image classification has been greatly promoted by deep neural networks. However,current deep learning-based PolSAR classification methods encounter difficulties due to its dependence on extensive labeled data and the computational inefficiency of architectures like Transformers. This paper presents ECP-Mamba, an efficient framework integrating multi-scale self-supervised contrastive learning with a state space model (SSM) backbone. Specifically, ECP-Mamba addresses annotation scarcity through a multi-scale predictive pretext task based on local-to-global feature correspondences, which uses a simplified self-distillation paradigm without negative sample pairs. To enhance computational efficiency,the Mamba architecture (a selective SSM) is first tailored for pixel-wise PolSAR classification task by designing a spiral scan strategy. This strategy prioritizes causally relevant features near the central pixel, leveraging the localized nature of pixel-wise classification tasks. Additionally, the lightweight Cross Mamba module is proposed to facilitates complementary multi-scale feature interaction with minimal overhead. Extensive experiments across four benchmark datasets demonstrate ECP-Mamba's effectiveness in balancing high accuracy with resource efficiency. On the Flevoland 1989 dataset, ECP-Mamba achieves state-of-the-art performance with an overall accuracy of 99.70%, average accuracy of 99.64% and Kappa coefficient of 99.62e-2. Our code will be available at https://github.com/HaixiaBi1982/ECP_Mamba.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
