基于上下文感知调制的高光谱图像深层特征提取网络
Going Deeper with Context-Aware Modulation for Hyperspectral Image Feature Extraction
摘要: 高光谱图像(HSI)由数百个连续窄带组成，具有丰富的光谱–空间信息。然而，波段间高度相关性与复杂依赖关系使得判别性光谱特征的高效建模仍具有挑战。为此，本文提出CAM-HSNet，旨在增强光谱局部特征与长距离依赖的联合建模能力的同时提升参数利用效率。具体而言，我们引入改进的卷积调制模块和卷积前馈网络，对卷积提取的局部光谱特征进行进一步编码，并构建能够捕获长距离依赖并实现信息交互的结构。在获得更具判别力的光谱表征基础上，模型能够以更少的谱维表示完成全局语义汇聚，从而压缩原有局部至全局映射的参数规模，实现参数减少且保持分类性能。实验结果表明，与现有方法相比，所提出的CAM-HSNet在整体精度(OA)、平均精度(AA)和Kappa系数等指标上均取得了更优的分类表现。
Abstract: Hyperspectral images (HSIs) consist of hundreds of contiguous narrow spectral bands and contain rich spectral-spatial information. However, the strong inter-band correlations and complex depen- dencies pose challenges for efficiently modeling discriminative spectral features. To address this issue, we propose CAM-HSNet, which aims to enhance the joint modeling of local spectral features and long-range dependencies while improving parameter efficiency. Specifically, we introduce an improved convolutional modulation module and a convolutional feed-forward network to further encode the locally extracted spectral features and to build structures capable of capturing long-range dependencies and facilitating information interaction. Based on the resulting more discriminative spectral representations, the model is able to achieve global semantic aggregation with fewer spectral dimensions, thereby reducing the parameter scale required for local-to-global mapping while maintaining classification performance. Experimental results demonstrate that, compared with existing methods, the proposed CAM-HSNet achieves superior performance in overall accuracy (OA), average accuracy (AA), and the Kappa coefficient.
文章引用：蔡士威. 基于上下文感知调制的高光谱图像深层特征提取网络[J]. 计算机科学与应用, 2026, 16(1): 215-229. https://doi.org/10.12677/csa.2026.161018

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