Building Extraction From High Spatial Resolution Remote Sensing Images of Complex Scenes by Combining Region-Line Feature Fusion and OCNN

Abstract

Building extraction from remote sensing imagery has been a research hotspot for some time with the advancement of AI in remote sensing. However, the edges of buildings extracted using existing techniques are commonly broken and inaccurate for the complex scenes in suburban and rural areas. This study proposes a framework for extracting structures by combining region-line feature fusion with object-based convolutional neural networks to solve this problem. First, a building edge detection network known as the Multichannel Attention-based Dense Extreme Inception Network for Edge Detection (MA-DexiNed) is constructed, which is considered more accurate for building edge extraction in complicated image scenes. Second, the probability map of the building edges obtained by MA-DexiNed is refined. According to rule judgment, breakpoints are linked by an edge thinning connection algorithm to obtain single-pixel, contiguous building line features. Third, the geometric boundaries of buildings are obtained by combining region attributes derived by unsupervised image segmentation and line features obtained from deep learning supervised segmentation. Finally, the pretrained AlexNet is employed to identify the class characteristics of buildings. The suggested framework was used for two GF-2 images and one Google Earth image from various regions and with numerous types of complicated scenes. The experimental findings demonstrated that this approach could extract more precise and complete building edges for complex image scenes compared with several existing methods. This advancement results from constrained regional image segmentation using deep semantic edge features. This methodology can offer a benchmark for subsequent building extraction tasks from high resolution imagery.