TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation
This work addresses the challenge of distinguishing easily confused classes in aerial image segmentation, which is incremental as it builds on existing CNN methods like DeepUNet and ResNeXt.
The paper tackles the problem of fine-grained semantic segmentation in subdecimeter aerial imagery by proposing TreeSegNet, an adaptive CNN that improves pixelwise classification for easily confused classes, achieving better results than state-of-the-art methods on the ISPRS Potsdam dataset.
For the task of subdecimeter aerial imagery segmentation, fine-grained semantic segmentation results are usually difficult to obtain because 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 the accuracy, few have paid attention to better differentiating the easily confused classes. In this paper, we propose TreeSegNet which adopts an adaptive network to increase the classification rate at the pixelwise level. Specifically, based on the infrastructure of DeepUNet, a Tree-CNN block in which each node represents a ResNeXt unit is constructed adaptively according to the confusion matrix and the proposed TreeCutting algorithm. By transporting feature maps through concatenating connections, the Tree-CNN block fuses multiscale features and learns best weights for the model. In experiments on the ISPRS 2D semantic labeling Potsdam dataset, the results obtained by TreeSegNet are better than those of other published state-of-the-art methods. Detailed comparison and analysis show that the improvement brought by the adaptive Tree-CNN block is significant.