Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation - Chengyi Wang; Lianfa Li (Remote Sensing, 2020)
It is challenging for semantic segmentation of buildings based on high-resolution remote sensing images, given high variability of appearance and complicated backgrounds of the buildings and their images. In this communication, we proposed an ensemble multi-scale residual deep learning method with the regularizer of shape representation for semantic segmentation of buildings. Based on the U-Net architecture using residual connections and multi-scale ASPP (atrous spatial pyramid pooling) modules, our method introduced the regularizer of shape representation and ensemble learning of multi-scale models to enhance model training and reduce over-fitting. In our method, the shape representation was coded in an antoencoder that was used to encode and reconstruct the shape characteristics of the buildings. In prediction, we consider multi-scale trained models for different resolution inputs and side effects to obtain an optimal semantic segmentation. With the high-resolution image of the Changshan, an island county in China, we used two-thirds of the study region image to train the model and the remaining one-third for the independent test. We obtained the accuracy of 0.98–0.99, mean intersection over union (MIoU) of 0.91–0.93 and Jaccard coefficient of 0.89–0.92 in validation. In the independent test, our method achieved state-of-the-art performance (MIoU: 0.83; Jaccard index: 0.81). By comparing with the existing representative methods on four different data sets, the proposed method consistently improved the learning process and generalization. The study shows important contributions of ensemble learning of multi-scale residual models and regularizer of shape representation to semantic segmentation of buildings.