Extraction of buildings from remote sensing imagery based on multi-scale SLIC-GMRF and FCNSVM

The extraction of buildings from remote sensing imagery has an important application value. However, high-resolution images contain detailed information and complex features that hinder the difficulty of building extraction process. To address this problem, we propose a building extraction method of building extraction based on multi-scale SLIC-GMRF and FCNSVM that demonstrates an improved ability of extracting buildings from high-resolution remote sensing images to some extent. First, a multi-scale SLIC-GMRF segmentation algorithm is applied to determine the initial building area, and then the advantages of the FCN neural network in semantic segmentation are utilized to extract the building features. Second, the extracted building features are used to train an SVM classifier to refine the building extraction results of building. The results of three control experiments and two comparative tests reveal that the SLIC segmentation algorithm affects the initial segmentation results, the SVM classifier affects the extraction of building details, and the FCN features influence the performance of the SVM classifier. The precision rate, recall rate, and quality index of the proposed method are all better than the compared methods. The following conclusions can be drawn from the experimental results. For the study area with clear features and minimal obstructions, the proposed method can effectively extract buildings from an image. This method can also obtain ideal results for areas with a complex distribution of buildings can also get ideal results. © 2020, Science Press. All right reserved.