Global Evolution Neural Network for Segmentation of Remote Sensing Images

The popular convolutional neural networks (CNNs) have been successfully used in very high-resolution remote sensing image semantic segmentation. However, these networks often suffer from performance limitations. First, although deeper networks usually provide better feature representation, they may cause parameter redundancy and the inefficient use of prior knowledge. Secondly, attention-based networks often only focus on weighting different features of a single sample but ignore the correlation of all samples in training set, thus leading to the loss of global information. To address above issues, we propose two simple yet effective global evolution strategies. The first is knowledge enhancement. This strategy can reactivate invalid convolutional kernels through convergence of different models and make full use of prior knowledge from the network to improve its feature representation. The second is a dict-attention module that greatly enhances the generalization of networks by learning and inferring the global relationship among different samples through the dictionary unit. As a result, a novel global evolution network (GENet) is designed based on knowledge enhancement and dict-attention for remote sensing image semantic segmentation. Experiments demonstrate that the proposed GENet is not only superior to popular networks in segmentation accuracy.