Accurate Building Extraction from Fused DSM and UAV Images Using a Chain Fully Convolutional Neural Network

Abstract

Accurate extraction of buildings using high spatial resolution imagery is essential to a wide range of urban applications. However, it is difficult to extract semantic features from a variety of complex scenes (e.g., suburban, urban and urban village areas) because various complex man-made objects usually appear heterogeneous with large intra-class and low inter-class variations. The automatic extraction of buildings is thus extremely challenging. The fully convolutional neural networks (FCNs) developed in recent years have performed well in the extraction of urban man-made objects due to their ability to learn state-of-the-art features and to label pixels end-to-end. One of the most successful FCNs used in building extraction is U-net. However, the commonly used skip connection and feature fusion refinement modules in U-net often ignore the problem of feature selection, and the ability to extract smaller buildings and refine building boundaries needs to be improved. In this paper, we propose a trainable chain fully convolutional neural network (CFCN), which fuses high spatial resolution unmanned aerial vehicle (UAV) images and the digital surface model (DSM) for building extraction. Multilevel features are obtained from the fusion data, and an improved U-net is used for the coarse extraction of the building. To solve the problem of incomplete extraction of building boundaries, a U-net network is introduced by chain, which is used for the introduction of a coarse building boundary constraint, hole filling, and "speckle" removal. Typical areas such as suburban, urban, and urban villages were selected for building extraction experiments. The results show that the CFCN achieved recall of 98.67%, 98.62%, and 99.52% and intersection over union (IoU) of 96.23%, 96.43%, and 95.76% in suburban, urban, and urban village areas, respectively. Considering the IoU in conjunction with the CFCN and U-net resulted in improvements of 6.61%, 5.31%, and 6.45% in suburban, urban, and urban village areas, respectively. The proposed method can extract buildings with higher accuracy and with clearer and more complete boundaries.

Building Footprint Extraction From Unmanned Aerial Vehicle Images Via PRU-Net: Application to Change Detection

“…For more information, see https://creativecommons.org/licenses/by/4.0/ accurate positioning and address image segmentation. Owing to its ability to acquire full resolution maps, U-Net has become a general framework for some of the state-of-the-art semantic segmentation methods [20], CNNs and U-Net have been widely used in building footprint extraction [21]- [23]. Yang et al [24] benefited from the scalability of CNNs and conducted a novel comparative analysis of four state-of-the-art CNNs for extracting building footprints throughout the United States.…”

Building Multi-Feature Fusion Refined Network for Building Extraction from High-Resolution Remote Sensing Images

“…It has an end-to-end pixel-level recognition capability and makes the semantic segmentation process easier to complete. Since then, the emphasis of research on building extraction from remote sensing images using deep learning technologies has shifted from CNNs to FCN [15][16][17][18]. In the optimization research of the building extraction method based on FCN, in order to improve the accuracy and integrity of building detection, the work mainly focuses on three aspects.…”

Progressive Data Augmentation Method for Remote Sensing Ship Image Classification Based on Imaging Simulation System and Neural Style Transfer

“…Following the success of deep learning and the increasing availability of remote sensing data, deep learning has been playing an increasingly important role in the field of remote sensing. With sufficient remote sensing data for training, researchers have focused on designing convolutional neural networks (CNNs) to perform feature selection [9], [10], [11], [12], extraction [13], [14], and coding [15] on high-resolution remote sensing images, thereby improving network performance. Meanwhile, remote sensing data also bring unprecedented challenges to deep learning.…”