Semantic segmentation of building extraction in very high resolution imagery via optimal segmentation guided by deep seeds

Abstract

The automatic segmentation of buildings from satellite images is extremely challenging due to the complex shapes of buildings. Deep convolutional neural networks (DCNNs) have recently enabled accurate pixel-level labeling tasks, which ensures precise boundaries. However, the large number of pixel-level labels required for DCNN-based semantic segmentation of buildings represents a considerable obstacle to the process. We propose a framework that relies on deep seeds and optimal segmentation to extract buildings from very high resolution imagery to solve the aforementioned issue. Input images are cropped rather than resized to a low-resolution image and passed directly to DCNN for segmentation. An image resize transformation will lead to a significant loss of image detail, especially for images with high resolution. For both image patches and resized images, a classification network is utilized to locate deep seeds within building and nonbuilding classes. Then, a boundary map can be predicted by passing the resized image through a convolutional oriented boundary network. A hierarchical segmentation tree is built to determine the optimal segmentation by seeking the optimal tree cut. The final segmentation is achieved by developing a graphic model representing a set of nodes that distribute information from deep seeds to unmarked regions. According to the experiments conducted on the ISPRS two-dimensional semantic labeling contest (Potsdam) and the WHU building datasets, the proposed framework is able to significantly enhance the building segmentation accuracy. We have achieved such improvements through a cost-effective computing method without training a segmentation network.