Building Extraction From Remote Sensing Images With DoG as Prior Constraint

Abstract

Building extraction from aerial and satellite remote sensing images is a basic component of social development. Compared to traditional feature extraction strategies, deep convolutional neural networks (CNNs) have the advantage of extracting deep high-level semantic features and efficient image processing capabilities. However, most works focus on developing end-to-end data-driven models without considering prior information, such as edges and the structures of buildings, which would cause loss of details and blurred boundaries in the prediction results. To alleviate this problem, we constructed a prior information module (PIM) as a constraint for feature map refinement in the training phase that uses the edge information extracted by the multiscale difference of Gaussian operator. We combined this module with the main network to form a dual-output training network. The module helps optimize the feature extraction process and solves the inaccurate and adhesion phenomenon of edge points in the building extraction results of the existing CNNs. Experiments illustrate that this PIM can improve the intersection over union of a lightweight network by 2.4% and 2.0% on an aerial remote sensing dataset and a satellite remote sensing dataset, respectively. The PIM can be embedded in other networks during the training phase to improve building extraction performance without any extra computational requirements during the inference phase. At the same time, we build a U-structure network with the proposed module, which outperforms another state-of-the-art building extraction approach with the extracted edge as prior information.

Keywords

• Building extraction
• deep learning
• difference of Gaussian (DoG) operator
• semantic segmentation