Paper Info
Title
A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery
Abstract
The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation by using deep neural networks in building extraction tasks. Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing. We propose a novel fully convolutional neural network called the Context Feature Enhancement Network (CFENet) to address these issues. CFENet comprises three modules: the spatial fusion module, the focus enhancement module, and the feature decoder module. First, the spatial fusion module aggregates the spatial information of low-level features to obtain buildings' outline and edge information. Secondly, the focus enhancement module fully aggregates the semantic information of high-level features to filter the information of building-related attribute categories. Finally, the feature decoder module decodes the output of the above two modules to segment the buildings more accurately. In a series of experiments on the WHU Building Dataset and the Massachusetts Building Dataset, our CFENet balances efficiency and accuracy compared to the other four methods we compared, and achieves optimality on all five evaluation metrics: PA, PC, F1, IoU, and FWIoU. This indicates that CFENet can effectively enhance and fuse buildings' low-level and high-level features, improving building extraction accuracy.
Year
DOI
Venue
2022
10.3390/rs14092276
REMOTE SENSING
Keywords
DocType
Volume
CFENet, fully convolutional neural network, remote sensing images, building extraction
Journal
14
Issue
ISSN
Citations 
9
2072-4292
0
PageRank 
References 
Authors
0.34
25
5
Name
Order
Citations
PageRank
Jinzhi Chen100.68
Dejun Zhang223819.97
Yiqi Wu301.69
Yilin Chen401.35
Xiaohu Yan511.70