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SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction

Li, Yang; Lu, Hui; Liu, Qi; Zhang, Yonghong; Liu, Xiaodong

Authors

Yang Li

Hui Lu

Qi Liu

Yonghong Zhang



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.

Journal Article Type Article
Acceptance Date Feb 4, 2022
Online Publication Date Feb 7, 2022
Publication Date 2022-02
Deposit Date Feb 14, 2022
Publicly Available Date Feb 14, 2022
Journal Remote Sensing
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 14
Issue 3
Article Number 768
DOI https://doi.org/10.3390/rs14030768
Keywords building extraction; dual-branch; semantic segmentation; encoder–decoder network
Public URL http://researchrepository.napier.ac.uk/Output/2844958

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