As a hot research topic in remote sensing, effectively integrating the advantageous features of multispectral and panchromatic images is the main challenge for fusing these two remote sensing images. This article proposes a multiscale frequency fusion network based on ConvGRU. To address the underutilization of texture features, we extract multiscale bandpass and low-pass sub-bands representing texture and content features through Contourlet decomposition. Multiscale bandpass sub-bands contain more comprehensive and concentrated texture details. Then, by proposing a multiscale frequency feature extractor based on ConvGRU, we effectively integrate and enhance sub-bands of different scales and frequencies, fully utilizing the characteristics of multispectral and panchromatic images and scale transmission. With these enhanced sub-band features, we obtain more comprehensive scale-enhanced texture features. Simultaneously, content features are also preserved as dual-source image features. Moreover, to reduce redundancy between fused features and make more efficient use of the obtained enhanced features, we designed an Inver-band integrator (IBI) module. It can fuse enhanced features at different scales, improve the complementarity between features, and thus achieve effective fusion. Experimental results demonstrate the effectiveness and robustness of our model on multiple datasets. Our codes are available at https://github.com/Xidian-AIGroup190726/GMFnet .

中文翻译：

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基于 ConvGRU 的多尺度频率融合网络，用于 PAN-MS 联合分类


作为遥感领域的热点研究课题，如何有效地融合多光谱和全色图像的优势特征是融合这两种遥感图像的主要挑战。本文提出了一种基于ConvGRU的多尺度频率融合网络。为了解决纹理特征利用不足的问题，我们通过 Contourlet 分解提取表示纹理和内容特征的多尺度带通和低通子带。多尺度带通子带包含更全面、更集中的纹理细节。然后，通过提出基于ConvGRU的多尺度频率特征提取器，有效地集成和增强不同尺度和频率的子带，充分利用多光谱全色图像和尺度传输的特性。通过这些增强的子带特征，我们获得了更全面的尺度增强纹理特征。同时，内容特征也被保留为双源图像特征。此外，为了减少融合特征之间的冗余并更有效地利用获得的增强特征，我们设计了一个带内积分器（IBI）模块。它可以融合不同尺度的增强特征，提高特征之间的互补性，从而实现有效的融合。实验结果证明了我们的模型在多个数据集上的有效性和鲁棒性。我们的代码可在 https://github.com/Xidian-AIGroup190726/GMFnet 获取。