提出新型双注意力结构，提升高光谱图像变化检测精度
Global and Local Attention-Based Transformer for Hyperspectral Image Change Detection
- 分全局与局部注意力模块，兼顾低频整体特征与高频细节
- 在三个数据集上优于现有方法，显著提升变化区域识别能力
- 适合从事遥感图像分析、变化检测的科研人员使用
近年来基于Transformer的高光谱图像（HSI）变化检测方法表现出色。然而，现有Transformer中的注意力机制在局部特征表达上存在局限。为此，本文提出全局与局部注意力融合的Transformer模型（GLAFormer），通过全局-局部注意力模块（GLAM）结合高频与低频信号。其中，全局注意力作用于下采样特征图以捕获低频信息，局部注意力采用非重叠窗口机制聚焦高频细节。同时引入交叉门控前馈网络（CGFN），通过并行路径的卷积与交叉门控机制增强显著特征、抑制噪声干扰。在三个高光谱图像数据集上的实验表明，该方法优于当前主流变化检测方法。代码已公开于 https://github.com/summitgao/GLAFormer。
原文摘要 · Abstract (English)
Recently Transformer-based hyperspectral image (HSI) change detection methods have shown remarkable performance. Nevertheless, existing attention mechanisms in Transformers have limitations in local feature representation. To address this issue, we propose Global and Local Attention-based Transformer (GLAFormer), which incorporates a global and local attention module (GLAM) to combine high-frequency and low-frequency signals. Furthermore, we introduce a cross-gating mechanism, called cross-gated feed-forward network (CGFN), to emphasize salient features and suppress noise interference. Specifically, the GLAM splits attention heads into global and local attention components to capture comprehensive spatial-spectral features. The global attention component employs global attention on downsampled feature maps to capture low-frequency information, while the local attention component focuses on high-frequency details using non-overlapping window-based local attention. The CGFN enhances the feature representation via convolutions and cross-gating mechanism in parallel paths. The proposed GLAFormer is evaluated on three HSI datasets. The results demonstrate its superiority over state-of-the-art HSI change detection methods. The source code of GLAFormer is available at \url{https://github.com/summitgao/GLAFormer}.
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