An improved U-Net method for the semantic segmentation of remote sensing images

Foremost deep neural network models trained in natural scenes cannot transfer and apply to remote sensing image semantic segmentation well. Studies have shown that fine-tuning methods containing model fusion can alleviate this dilemma. In this paper, we provide an approach used to improve U-Net and propose an end-to-end deep convolutional neural network (DCNN) combining the superiorities of DenseNet, U-Net, dilated convolution, and DeconvNet. We evaluated the proposed method and model on the Potsdam orthophoto data set. Compared with U-Net, our approach increases the PA, mPA, and mIoU evaluation indexes by 11.1%, 14.0%, and 13.5%, respectively; the segmentation speed increases by approximately 1.18 times and the number of parameters is 59.0% that of U-Net. The experiments demonstrate that for the semantic segmentation of high-resolution remote sensing images, using the combined dilated convolutions as the primary feature extractor, using the transposed convolution to restore the size of the feature maps, and reducing the number of layers is an effective method to improve the comprehensive performance of U-Net. This research enriches the models based on DCNNs and the modes of using DCNNs in a specific scene.