Research on Building Extraction based on Neural Network with Feature Enhancement and ELU Activation Function

In recent years, with the rapid development of the city, a large number of people turn to work and live in the city, resulting in an increasing number of urban buildings. Land resources and urban ecological environment (such as green space) are threatened to some extent. Thus, it is urgent to plan urban land resources and space reasonably, prevent illegal construction, improve urban living environment, and make the city sustainable, orderly, healthy, and green. With the high-resolution remote sensing image data becoming more and more abundant, accurate building extraction using high-resolution remote sensing images plays an important role in urban planning, urban management, and change detection of urban buildings. Based on the U-Net network model, using the Massachusetts building dataset, this paper explored the network model structure and proposed a network model called FE-Net with "encoder-feature enhancement-decoder" structure and ELU activation function. First, the best basic network model called U-Net6 was found by comparing the building extraction results using U-Net5, U-Net6, and U-Net7 with different number of network layers. Based on the U-Net6, the network model of "U-Net6+ReLU+feature enhancement" was established by adding the structure of feature enhancement. In order to optimize the activation function, the ReLU activation function was replaced by the ELU activation function, and then the network model called FE-Net (U-Net6+ELU+feature enhancement) was created. The FE-Net network model was compared with the building extraction results from the other two network models (U-Net6+ReLU and U-Net6+ReLU+feature enhancement). Results show that the FE-Net network model had the best building extraction performance. Its relaxed F1-measure reached 97.23%, which was 0.36% and 0.12% higher than the other two network models. Meanwhile, FE-Net also had the highest extraction accuracy compared with other studies using the same dataset of Massachusetts. The FE-Net network model can extract multi-scale buildings better, which can not only extract small-scale buildings accurately, but also roughly and completely extract buildings with irregular shape with relatively less missing and wrong detections. Thus, the FE-Net network model can be used to achieve end-to-end building extraction with a high accuracy. © 2021, Science Press. All right reserved.