Prefab-Net: Segmentation of prefabricated building from satellite imagery with deep neural network

Abstract

The development of deep learning techniques such as convolutional neural networks (CNN) has injected new vitality into the study of semantic segmentation. Benefiting from the development of new technology, the automatic and intelligent work of remote sensing image segmentation has also entered a new stage. With the further improvement of China’s urbanization rate, urban construction is also developing rapidly. One of the most important tasks in land surveillance is to monitor buildings above ground using remote sensing images. Prefabricated building is the most common type of illegal building in developing area. At present, supervision mainly relies on the traditional human-centered method, which is time-consuming and low in efficiency. Whether the deep learning methods can benefit the prefabricated building segmentation has not yet been tested. By taking the realistic demand into account, this paper proposes a customized neural network structure, which combines multiple refined modules and improves the feature extraction, especially for the prefabricated building segmentation task. Through the comparison with the baseline algorithm, the specifically refined model for the prefabricated building has shown better results in our experimental area, which proves the feasibility and superiority in practical application.