Segmentation of Aerial Image with Multi-scale Feature and Attention Model

Aerial image labeling plays an important part in the mapping of maps with high precision. The knowledge about the range and intensive degree of aerial building segmentation is necessary for urban planning. Fully convolutional networks (FCNs) have recently shown state-of-the-art performance in image segmentation. In order to get better aerial images segmentation performance, we use a method of combing FCNs with multi-scale features and attention model in order to carry out segmentation automatically in aerial images. Attention model gives each scale feature added extra supervision to achieve better segmentation. Here, U-net and FCN-8s are used as original semantic segmentation model to train with multi-scale images and attention models. The datasets use different proportions of Inria Aerial Image Labeling Dataset, including two semantic classes: building and not building. The results show that the semantic segmentation model combined with multi-scale features and attention model has higher segmentation accuracy and better performance.