遥感图像语义分割空间全局上下文信息网络

为了解决卷积神经网络(CNN)在语义分割特征提取阶段容易丢失空间信息以及边界信息不明确的问题，基于U-Net基线网络提出空间全局上下文信息网络(NC-Net). 增加再编码阶段(ReEncoder)，以增强空间信息识别能力. 在Decoder阶段输出多尺度特征，与ReEncoder阶段结合获取全局上下文信息. 保留边界损失函数，设计多尺度损失函数级联方法，优化整体网络. 在GID以及WHDLD数据集上的实验结果表明，该方法的总体准确度达到最好成绩，明显优于其他基线模型.

Spatial global context information network for semantic segmentation of remote sensing image

A spatial global context information network (NC-Net) was proposed based on the U-Net baseline network in order to solve the problem that the convolutional neural network (CNN) easily lost spatial information and the boundary information was unclear in the feature extraction stage of semantic segmentation. A re-encoding stage was added (ReEncoder) in order to enhance the ability of spatial information recognition. Multi-scale features were output in the Decoder stage, which was combined with the ReEncoder stage to obtain global context information. The boundary loss function was retained, and a multi-scale loss function cascade method was designed to optimize the overall network. The experimental results on the GID and WHDLD data sets show that the overall accuracy of the method achieves the best results, significantly outperforming other baseline models.