The description of context information affected by speckle and class imbalance under labeled data makes the pixelwise classification for high-resolution (HR) synthetic aperture radar (SAR) image a challenging task. To address these issues, we propose a global-context pyramidal and class-balanced network (GPCNet) for HR SAR image classification. The proposed structure follows an encoder–decoder architecture. In the encoder module, the multiscale convolutional and global-local cross-channel attention (GCA) blocks are employed to capture the global-context and distinguishable deep feature statistics, while reducing the impacts of the random fluctuation in the homogeneous region. The channel information of different scale convolutional layers is efficiently learned by local cross-channel interaction in the GCA block. Besides, a sampled class-balanced loss, associating with the effective number, is utilized for alleviating the class imbalance of HR SAR images. The experiments carried out on a TerraSAR-X image classification dataset demonstrate GPCNet is able to yield superior performance when compared with other related networks.

中文翻译：

---

GPCNet：用于高分辨率 SAR 遥感图像分类的全局上下文金字塔和类平衡网络

标记数据下受散斑和类别不平衡影响的上下文信息的描述使得高分辨率（HR）合成孔径雷达（SAR）图像的像素分类成为一项具有挑战性的任务。为了解决这些问题，我们提出了一种用于 HR SAR 图像分类的全局上下文金字塔和类平衡网络 (GPCNet)。所提出的结构遵循编码器-解码器架构。在编码器模块中，采用多尺度卷积和全局局部跨通道注意 (GCA) 块来捕获全局上下文和可区分的深度特征统计信息，同时减少均匀区域中随机波动的影响。通过 GCA 块中的局部跨通道交互有效地学习不同尺度卷积层的通道信息。除了，与有效数量相关的采样类平衡损失用于缓解 HR SAR 图像的类不平衡。在 TerraSAR-X 图像分类数据集上进行的实验表明，与其他相关网络相比，GPCNet 能够产生卓越的性能。