High-resolution remote sensing image semantic segmentation based on a deep feature aggregation network

Semantic segmentation of high-resolution remote sensing images has a wide range of applications, such as territorial planning, geographic monitoring and smart cities. The proper operation of semantic segmentation for remote sensing images remains challenging due to the complex and diverse transitions between different ground areas. Although several convolution neural networks (CNNs) have been developed for remote sensing semantic segmentation, the performance of CNNs is far from the expected target. This study presents a deep feature aggregation network (DFANet) for remote sensing image semantic segmentation. It is composed of a basic feature representation layer, an intermediate feature aggregation layer, a deep feature aggregation layer and a feature aggregation module (FAM). Specially, the basic feature representation layer is used to obtain feature maps with different resolutions: the intermediate feature aggregation layer and deep feature aggregation layer can fuse various resolution features and multi-scale features; the FAM is used to splice the features and form more abundant spatial feature maps; and the conditional random field module is used to optimize semantic segmentation results. We have performed extensive experiments on the ISPRS two-dimensional Vaihingen and Potsdam remote sensing image datasets and compared the proposed method with several variations of semantic segmentation networks. The experimental results show that DFANet outperforms the other state-of-the-art approaches.