Multi-scale network for remote sensing segmentation

The semantic segmentation of remote sensing images is a critical and challenging task. How to easily and reliably segment useful information from vast remote sensing images is a significant issue. Many methods based on convolutional neural networks have been widely explored to obtain more accurate segmentation from remote sensing images. However, due to the uniqueness of remote sensing images, such as the dramatic changes in the scale of the target object, the results are not satisfactory. To solve the problem, a special network is designed: (1) Create a new backbone network. Compared with ResNet50, the proposed method extracts features of varying sizes more effectively. (2) Reduce spatial information loss. Building a hybrid location module to compensate for the position loss caused by the down-sampling operation. (3) Models with high discriminant ability. In order to improve the discrimination ability of the model, a novel auxiliary loss function is designed to constrain the distance between inter-class and intra-class. The proposed algorithm is tested on remote sensing datasets (e.g., NWPU-45, DLRSD, and WHDLD). The experimental results show that this method obtains the best results and achieves state-of-the-art performance.