High-resolution remote sensing images semantic segmentation using improved UNet and SegNet

Abstract

Semantic segmentation for high-resolution (HR) remote sensing (RS) images under the condition of a small number of training samples with imprecise labels is a challenging task. In this paper, we propose a novel method based on improved UNet and SegNet. First, a batch normalization layer is introduced into the original UNet to accelerate the convergence speed and an ELU activation function is selected instead of ReLU to avoid the neuron death. Second, to enhance the conventional SegNet, the encoder is reconstructed and a skip connection is designed to reuse the deep features, which is also beneficial for the network convergence. Third, a joint model is constructed by combining the improved UNet and SegNet and meanwhile a voting strategy is proposed to compute the final results. Experiments on the real-world HR RS images verify the effectiveness and superiority of the proposed method.