BCG-Former：通过波段上下文门控实现帕累托有效高光谱图像分类

中文摘要 AI 辅助

高光谱图像（HSI）分类系统越来越多地部署在计算预算严格的平台上，如无人机和小型星载传感器。在这些情况下，仅准确性是不够的，模型还必须在严格的延迟和内存限制内运行。然而，最近的HSI分类器大多关注准确性，而对这些限制关注较少。我们提出了BCG-Former，一种轻量级的CNN-Transformer混合模型来解决这种权衡。该模型引入了三项创新：用于使用局部波段间上下文和可学习温度锐化进行自适应光谱重新校准的波段上下文门控（BCG）；连接光谱和空间特征的光谱摘要令牌；结合线性注意力的单通道波段旋转位置编码（Band-RoPE）以进行高效的联合表示学习。在经典机载（帕维亚大学、萨利纳斯、印第安松、休斯顿2013/2018）和无人机载基准数据集（WHU-Hi-LongKou、洪湖、汉川）上进行评估，BCG-Former的总体准确率在休斯顿2018上为91.51%，在休斯顿2013上为99.49%，同时保持亚毫秒级推理延迟（0.91-0.95毫秒），仅使用0.10-0.23M参数。在所有八个基准测试中，BCG-Former始终位于或接近准确性与延迟的帕累托前沿，以其计算成本的一小部分优于或匹配最近基于CNN、Transformer和曼巴的方法。消融研究证实所有三个组件是互补的，BCG提供了最大的个体贡献。这些结果确立了BCG-Former作为实时和大规模遥感应用中强大的准确性-效率帕累托候选模型。

英文摘要

Hyperspectral image (HSI) classification systems are increasingly deployed on platforms with strict computational budgets, such as UAVs and small spaceborne sensors. In these settings, accuracy alone is not enough; the model must also run within tight latency and memory constraints. Most recent HSI classifiers, however, focus on accuracy and pay relatively little attention to these constraints. We propose BCG-Former, a lightweight CNN-Transformer hybrid that targets this trade-off. The model introduces three innovations: (1) Band-Contextual Gating (BCG) for adaptive spectral recalibration using local inter-band context and learnable temperature sharpening, (2) a spectral summary token that bridges spectral and spatial features, and (3) single-pass Band-RoPE combined with linear attention for efficient joint representation learning. Evaluated on classical airborne (Pavia University, Salinas, Indian Pines, Houston 2013/2018) and UAV-borne benchmark datasets (WHU-Hi-LongKou, HongHu, and HanChuan), BCG-Former achieves over-all accuracy ranging from 91.51% on Houston 2018 to 99.49% on Houston 2013, while maintaining sub-millisecond inference latency (0.91-0.95ms) and using only 0.10-0.23M parameters. Across all eight benchmarks, BCG-Former consistently resides on or near the Pareto frontier of accuracy versus latency, outperforming or matching recent CNN-, Transformer-, and Mamba-based methods at a fraction of their computational cost. Ablation studies confirm that all three components are complementary, with BCG providing the largest individual contribution. These results establish BCG-Former as a strong accuracy-efficiency Pareto candidate for real-time and large-scale remote sensing applications.

发表机构

• College of Information Science, University of Arizona（亚利桑那大学信息科学学院）
• Department of Electrical and Computer Engineering, University of Arizona（亚利桑那大学电气与计算机工程系）

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