ConvVitMamba：高效的多尺度卷积、Transformer和Mamba序列建模用于超光谱图像分类
ConvVitMamba: Efficient Multiscale Convolution, Transformer, and Mamba-Based Sequence modelling for Hyperspectral Image Classification
AI总结：
本文提出ConvVitMamba框架，结合多尺度卷积、Transformer和轻量Mamba模块，有效解决超光谱图像分类中的高维、冗余和数据不足问题，实现高精度与低开销的平衡。
摘要：
超光谱图像（HSI）分类面临高光谱维度、冗余和标注数据有限的挑战。尽管CNN和Vision Transformers通过利用光谱-空间信息和长距离依赖性表现出色，但它们通常计算成本高且模型体积大，限制了实际应用。为此，提出了一种统一的混合框架ConvVitMamba，整合多尺度卷积特征提取器、基于Transformer的分块和编码阶段以及轻量级Mamba启发的门控序列混合模块。主成分分析（PCA）用于预处理以减少冗余并提高效率。在四个基准数据集上进行的实验表明，ConvVitMamba在保持精度、模型大小和推理效率之间取得良好平衡，优于CNN、Transformer和Mamba方法。消融研究证实了所有组件的互补贡献。结果表明，所提框架为不同场景下的HSI分类提供了有效且高效的解决方案。源代码可在https://github.com/mqalkhatib/ConvVitMamba公开获取。
英文摘要：
Hyperspectral image (HSI) classification remains challenging due to high spectral dimensionality, redundancy, and limited labeled data. Although convolutional neural networks (CNNs) and Vision Transformers (ViTs) achieve strong performance by exploiting spectral-spatial information and long-range dependencies, they often incur high computational cost and large model size, limiting practical use. To address these limitations, a unified hybrid framework, termed ConvVitMamba, is proposed for efficient HSI classification. The architecture integrates three components: a multiscale convolutional feature extractor to capture local spectral, spatial, and joint patterns; a Vision Transformer based tokenization and encoding stage to model global contextual relationships; and a lightweight Mamba inspired gated sequence mixing module for efficient content-aware refinement without quadratic self-attention. Principal Component Analysis (PCA) is used as preprocessing to reduce redundancy and improve efficiency. Experiments on four benchmark datasets, including Houston and three UAV borne QUH datasets (Pingan, Qingyun, and Tangdaowan), demonstrate that ConvVitMamba consistently outperforms CNN, Transformer, and Mamba based methods while maintaining a favorable balance between accuracy, model size, and inference efficiency. Ablation studies confirm the complementary contributions of all components. The results indicate that the proposed framework provides an effective and efficient solution for HSI classification in diverse scenarios. The source code is publicly available at https://github.com/mqalkhatib/ConvVitMamba
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。
