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English(EN) Direction-adaptive Mamba: Spatial-Frequency Dual-Domain Collaborative Learning for PolSAR Image Classification

新的DA-Mamba框架增强了PolSAR图像分类

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了DA-Mamba，一种用于极化合成孔径雷达（PolSAR）图像分类的新型框架。该新架构通过引入方向自适应扫描来解决现有基于Mamba的方法的局限性，以更好地捕捉对PolSAR分析至关重要的各向异性散射和弱边界。DA-Mamba还采用非下采样轮廓波变换（NSCT）在空间和频率域处理数据，将全局分量与定向高频特征相结合，以增强可区分性。在三个真实PolSAR数据集上的实验表明，DA-Mamba的性能优于当前最先进的方法。 AI

影响 引入了一种新颖的深度学习架构，以改进专业图像数据的分析。

排序理由 详细介绍图像分类新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的DA-Mamba框架增强了PolSAR图像分类

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详细介绍图像分类新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Junfei Shi, Yu Cheng, Haojia Zhang, Wenqiang Hua, Junhuai Li, Maoguo Gong ·

    方向自适应Mamba：SAR图像分类的空频双域协同学习

    arXiv:2607.23464v1 Announce Type: cross Abstract: Deep learning dominates polarimetric synthetic aperture radar (PolSAR) image classification, with Mamba architectures serving as favorable backbones due to linear complexity and strong global modeling capacity. However, existing P…

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