Towards Accurate High Resolution Satellite Image Semantic Segmentation

Abstract

Satellite image semantic segmentation, including extracting road, detecting building, and identifying land cover types, is essential for sustainable development, agriculture, forestry, urban planning, and climate change research. Nevertheless, it is still unclear how to develop a refined semantic segmentation model in an efficient and elegant way. In this paper, we propose attention dilation-LinkNet (AD-LinkNet) neural network that adopts encoder-decoder structure, serial-parallel combination dilated convolution, channel-wise attention mechanism, and pretrained encoder for semantic segmentation. Serial-parallel combination dilated convolution enlarges receptive field as well as assemble multi-scale features for multi-scale objects, such as long-span road and small pool. The channel-wise attention mechanism is designed to advantage the context information in the satellite image. The experimental results on road extraction and surface classification data sets prove that the AD-LinkNet shows a significant effect on improving the segmentation accuracy. We defeated the D-Linknet algorithm that won the first place in the CVPR 2018 DeepGlobe road extraction competition.