Look at the Big Picture: Building Area Extraction with Global Density Map

Haowen Guo, Wensheng Cheng, Wen Yang, Guisong Xia

Abstract: The automatic extraction of building areas from high-resolution satellite imagery has become an important and challenging research issue. Many recent studies have explored different deep learning-based semantic segmentation methods for better accuracy. However, the deep network usually takes sliding window cropped satellite images as inputs, which loses the global information and causes a high false positive rate. In this paper, we propose a density map guided attention mechanism for building area extraction to make the network look at the big picture. We exploit an FCN-based building density prediction network to generate a density heatmap from large satellite images. The density factors in heatmap control the classifier's threshold of building area extraction network that optimize the FP and recall rates. Furthermore, we propose a test-time overlap augmentation mechanism to improve the segmentation results. Our method outperforms state-of-the-art approaches and increases mIoU by about 3.08% to 93.31%, and decreases FP rate to 0.91%.