Paper Info
Title
SSDBN: A Single-Side Dual-Branch Network with Encoder-Decoder for Building Extraction
Abstract
In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for the statistical work of a building area. Recent research efforts have been devoted to semantic segmentation using deep learning approaches, which can be further divided into two aspects. In this paper, we propose a single-side dual-branch network (SSDBN) based on an encoder-decoder structure, where an improved Res2Net model is used at the encoder stage to extract the basic feature information of prepared images while a dual-branch module is deployed at the decoder stage. An intermediate framework was designed using a new feature information fusion methods to capture more semantic information in a small area. The dual-branch decoding module contains a deconvolution branch and a feature enhancement branch, which are responsible for capturing multi-scale information and enhancing high-level semantic details, respectively. All experiments were conducted using the Massachusetts Buildings Dataset and WHU Satellite Dataset I (global cities). The proposed model showed better performance than other recent approaches, achieving an F1-score of 87.69% and an IoU of 75.83% with a low network size volume (5.11 M), internal parameters (19.8 MB), and GFLOPs (22.54), on the Massachusetts Buildings Dataset.
Year
DOI
Venue
2022
10.3390/rs14030768
REMOTE SENSING
Keywords
DocType
Volume
building extraction, dual-branch, semantic segmentation, encoder-decoder network
Journal
14
Issue
Citations 
PageRank 
3
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Yang Li100.34
Hui Lu200.34
Qi Liu300.34
Yonghong Zhang473.89
Xiaodong Liu501.35