In recent years, the panchromatic (PAN) and the multispectral (MS) remote sensing images classification has become a research hotspot. In this paper, we propose a spatial-channel progressive fusion residual network (SCPF-ResNet) for multi-resolution remote sensing classification. Firstly, for the inputs of the proposed network, the interactive data fusion strategy (IDFS) combines generalized-intensity-hue-saturation (GIHS), and discrete wavelet transform (DWT) to interfuse patch pairs of the PAN and the MS images, so as to increase the similarity between them, thus reduce the difference in information between them. Secondly, for the branches of feature extraction, we design an adaptive spatial attention module (ASA-Module) and an adaptive channel attention module (ACA-Module) to strengthen spatial features from both larger-sized with smaller-sized targets and spectral features among channels. Finally, we insert the ASA-Module and ACA-Module into the residual modules to form a triple-branch network and use the common spatial-channel features extracted by the Fusion_Branch to gradually enhance the pure independent features extracted by the PAN_Branch and the MS_Branch, respectively. The experimental results indicate that SCPF-ResNet can achieve competitive performance.

近年来，全色（PAN）和多光谱（MS）遥感图像分类已成为研究热点。在本文中，我们提出了一种用于多分辨率遥感分类的空间信道渐进融合残差网络（SCPF-ResNet）。首先，对于所提出的网络的输入，交互式数据融合策略（IDFS）将广义强度-色相饱和度（GIHS）和离散小波变换（DWT）相结合，以融合PAN和MS图像的补丁对，因此这样可以增加它们之间的相似度，从而减少它们之间的信息差异。其次，对于特征提取的分支，我们设计了一个自适应空间注意模块（ASA-Module）和一个自适应通道注意模块（ACA-Module），以增强较大目标和较小目标的空间特征以及通道之间的频谱特征。最后，我们将ASA模块和ACA模块插入残差模块以形成三分支网络，并使用Fusion_Branch提取的公共空间通道特征逐步增强PAN_Branch和MS_Branch提取的纯独立特征，分别。实验结果表明，SCPF-ResNet可以实现竞争性能。我们将ASA模块和ACA模块插入残差模块以形成三分支网络，并使用Fusion_Branch提取的公共空间通道特征逐步增强分别由PAN_Branch和MS_Branch提取的纯独立特征。实验结果表明，SCPF-ResNet可以实现竞争性能。我们将ASA模块和ACA模块插入残差模块以形成三分支网络，并使用Fusion_Branch提取的公共空间通道特征逐步增强分别由PAN_Branch和MS_Branch提取的纯独立特征。实验结果表明，SCPF-ResNet可以实现竞争性能。