PAIRWISE STEREO IMAGE DISPARITY AND SEMANTICS ESTIMATION WITH THE COMBINATION OF U-NET AND PYRAMID STEREO MATCHING NETWORK

Stereo images are one of the most common resources for 3D reconstruction. In the pairwise semantic stereo challenge of the 2019 IEEE GRSS Data Fusion Contest, we generate the classification and the disparity map based on U-Net and Pyramid Stereo Matching Network (PSMNet), respectively. By using a dynamic and class-weighted loss function, the U-Net is effectively trained with the imbalanced training samples. By voting classification results of the augmented prediction data with models trained under different epochs, we further refine the classification maps with the constraints of pseudo DSM, water index and mean-shift segmentation.