Deep convolutional encoder-decoder networks based on ensemble learning for semantic segmentation of high-resolution aerial imagery

Due to the complexity of object information and optical conditions of high-resolution aerial imagery, it is difficult to obtain fine semantic segmentation performance. Although various deep neural network structures have been proposed to improve segmentation accuracy, there is still room for improving accuracy by making full use of multiscale features and integrating these single weak classifiers into a strong classifier. In this paper, we use a reduced SegNet network to realize the end-to-end classification of high-resolution aerial images. In addition, to use multiscale information, we present the R-SegUnet which combines the feature information of each convolution block in the reduced SegNet encoding network with the feature information of the corresponding convolution block in the decoding network. Furthermore, considering that the surface features in high-resolution aerial images are very complex, we investigate a 6to2_Net that converts the six-classification model into six binary-classification models for the recognition effect on small objects. Finally, we ensemble the above three different models to get the segmentation results. Experiment results on ISPRS Potsdam benchmark dataset show that our algorithm is state-of-the-art method. We also analyze the inference performance of our models on a variety of parallel computing devices.