提出自适应卷积网络，提升遥感图像融合的细节与保真度。
RAPNet: A Receptive-Field Adaptive Convolutional Neural Network for Pansharpening
- 用局部自适应卷积核捕捉图像内容差异，动态调整感受野。
- 在多个公开数据集上超越现有方法，保持更高空间细节与光谱保真度。
- 适合遥感图像处理、高分辨率影像融合的研究者与工程师。
多光谱图像与全色图像融合（即图像超分）是遥感中的关键任务。尽管卷积神经网络表现良好，但其固定卷积核难以适应不同位置的局部内容变化。为此，本文提出RAPNet，引入内容自适应卷积机制。核心为感受野自适应融合卷积（RAPConv），能根据局部特征动态生成空间可变卷积核，提升空间细节提取精度。同时设计全色动态特征融合模块（PAN-DFF），结合注意力机制，在增强空间细节与保持光谱保真之间取得平衡。在多个公开数据集上的实验表明，该方法在定量指标和定性视觉效果上均优于现有方法。消融实验进一步验证了自适应模块的有效性。
原文摘要 · Abstract (English)
Pansharpening refers to the process of integrating a high resolution panchromatic (PAN) image with a lower resolution multispectral (MS) image to generate a fused product, which is pivotal in remote sensing. Despite the effectiveness of CNNs in addressing this challenge, they are inherently constrained by the uniform application of convolutional kernels across all spatial positions, overlooking local content variations. To overcome this issue, we introduce RAPNet, a new architecture that leverages content-adaptive convolution. At its core, RAPNet employs the Receptive-field Adaptive Pansharpening Convolution (RAPConv), designed to produce spatially adaptive kernels responsive to local feature context, thereby enhancing the precision of spatial detail extraction. Additionally, the network integrates the Pansharpening Dynamic Feature Fusion (PAN-DFF) module, which incorporates an attention mechanism to achieve an optimal balance between spatial detail enhancement and spectral fidelity. Comprehensive evaluations on publicly available datasets confirm that RAPNet delivers superior performance compared to existing approaches, as demonstrated by both quantitative metrics and qualitative assessments. Ablation analyses further substantiate the effectiveness of the proposed adaptive components.
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