AFGL-Net: Attentive Fusion of Global and Local Deep Features for Building Façades Parsing

Autor: Dong Chen, Guiqiu Xiang, Jiju Peethambaran, Liqiang Zhang, Jing Li, Fan Hu
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Remote Sensing, Vol 13, Iss 24, p 5039 (2021)
Druh dokumentu: article
ISSN: 2072-4292
DOI: 10.3390/rs13245039
Popis: In this paper, we propose a deep learning framework, namely AFGL-Net to achieve building façade parsing, i.e., obtaining the semantics of small components of building façade, such as windows and doors. To this end, we present an autoencoder embedding position and direction encoding for local feature encoding. The autoencoder enhances the local feature aggregation and augments the representation of skeleton features of windows and doors. We also integrate the Transformer into AFGL-Net to infer the geometric shapes and structural arrangements of façade components and capture the global contextual features. These global features can help recognize inapparent windows/doors from the façade points corrupted with noise, outliers, occlusions, and irregularities. The attention-based feature fusion mechanism is finally employed to obtain more informative features by simultaneously considering local geometric details and the global contexts. The proposed AFGL-Net is comprehensively evaluated on Dublin and RueMonge2014 benchmarks, achieving 67.02% and 59.80% mIoU, respectively. We also demonstrate the superiority of the proposed AFGL-Net by comparing with the state-of-the-art methods and various ablation studies.
Databáze: Directory of Open Access Journals
Nepřihlášeným uživatelům se plný text nezobrazuje