Ensemble of Training Models for Road and Building Segmentation

In this paper, we propose an object segmentation method in satellite images by the ensemble of models obtained through training process. To improve recognition accuracy, the ensemble of models obtained by different random seeds is used. Here we pay attention to the ensemble of models obtained through training process. In model ensemble, we should integrate the models with different opinions. Since the pixels with low probability such as boundary are often updated through training process, each model in training process has different probability for boundary regions, and the ensemble of those probability maps is effective for improving segmentation accuracy. Effectiveness of the ensemble of training models is demonstrated by experiments on building and road segmentation. Our proposed method improved approximately 4% in comparison with the best model selected by validation. Our method also achieved better accuracy than the standard ensemble of models.