Refined Semantic Segmentation Network of City Remote Sensing Image

Abstract

Semantic segmentation is always a key problem in remote sensing image analysis. Especially, it is very useful for city-scale vehicle detection. However, multi-object and imbalanced data classes of remote sensing images bring a huge challenge, which leads that many traditional segmentation approaches were often unsatisfactory. In this paper, we propose a novel Refined Semantic Segmentation Network (R2SN), which apply the classic encoder-to-decoder framework to handle segmentation problem. However, we add the convolution layers in encoder and decoder to make the network can achieve more local information in the training step. The design is more suitable for high-resolution remote sensing image. More specially, the classic Focal loss is introduced in this network, which can guide the model focus on the difficult objects in remote sensing images and effectively handle multi-object segmentation problem. Meanwhile, the classic Hinge loss is also utilized to increase the distinction between classes, which can guarantee the more refined segmentation results. We validate our approach on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation benchmark dataset. The evaluation and comparison results show that our method exceeds the state-of-the-art remote sensing image segmentation methods in terms of mean intersection over union (MIoU), pixel accuracy, and F1-score.