TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation

Fine-grained semantic segmentation results are typically difficult to obtain for subdecimeter aerial imagery segmentation as a result of complex remote sensing content and optical conditions. Recently, convolutional neural networks (CNNs) have shown outstanding performance on this task. Although many deep neural network structures and techniques have been applied to improve accuracy, few have attended to improving the differentiation of easily confused classes. In this paper, we propose TreeUNet, a tool that uses an adaptive network to increase the classification rate at the pixel level. Specifically, based on a deep semantic model infrastructure, a Tree-CNN block in which each node represents a ResNeXt unit is constructed adaptively in accordance with the confusion matrix and the proposed TreeCutting algorithm. By transmitting feature maps through concatenating connections, the Tree-CNN block fuses multiscale features and learns best weights for the model. In experiments on the ISPRS two-dimensional Vaihingen and Potsdam semantic labelling datasets, the results obtained by TreeUNet are competitive among published state-of-the-art methods. Detailed comparison and analysis show that the improvement brought by the adaptive Tree-CNN block is significant.