DR-Net: An Improved Network for Building Extraction from High Resolution Remote Sensing Image

At present, convolutional neural networks (CNN) have been widely used in building extraction from remote sensing imagery (RSI), but there are still some bottlenecks. On the one hand, there are so many parameters in the previous network with complex structure, which will occupy lots of memories and consume much time during training process. On the other hand, low-level features extracted by shallow layers and abstract features extracted by deep layers of artificial neural network cannot be fully fused, which leads to an inaccurate building extraction from RSI. To alleviate these disadvantages, a dense residual neural network (DR-Net) was proposed in this paper. DR-Net uses a deeplabv3+Net encoder/decoder backbone, in combination with densely connected convolution neural network (DCNN) and residual network (ResNet) structure. Compared with deeplabv3+net (containing about 41 million parameters) and BRRNet (containing about 17 million parameters), DR-Net contains about 9 million parameters; So, the number of parameters reduced a lot. The experimental results for both the WHU Building Dataset and Massachusetts Building Dataset, DR-Net show better performance in building extraction than other two state-of-the-art methods. Experiments on WHU building data set showed that Intersection over Union (IoU) increased by 2.4% and F1 score increased by 1.4%; in terms of Massachusetts Building Dataset, IoU increased by 3.8% and F1 score increased by 2.9%.

Automated summary

In this study, a dense residual neural network (DR-Net) was proposed for building extraction from remote sensing imagery. DR-Net outperformed other state-of-the-art methods in terms of Intersection over Union (IoU) and F1 score on both WHU Building Dataset and Massachusetts Building Dataset. The experimental results showed significant improvements in accuracy compared to existing methods.