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.