Comparative research on different backbone architectures of DeepLabV3+ for building segmentation

Saziye Ozge Atik*, Muhammed Enes Atik, Cengizhan Ipbuker

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkıMakalebilirkişi

24 Atıf (Scopus)

Özet

Rapid urban growth and globalization affect land use in cities, and the need for automatic interpretation of remote sensing images is constantly increasing. Deep neural networks are becoming widespread in high-resolution aerial and satellite image sources in Earth observation missions. Various convolutional neural network (CNN) architectures have been implemented in building extraction, but it is still challenging to distinguish building class from other man-made classes in public datasets. Here, we present comparative research for automatic building extraction on different data sources using DeepLabV3+ architecture with ResNet-18, ResNet-50, Xception, and MobileNetv2 models. The CNNs are implemented on Inria Aerial Image Labeling, Massachusetts Buildings, and Wuhan University Building Extraction Datasets in terms of evaluation metrics and training and testing time consumption. Our implementation of the DeepLabV3 + ResNet-50 model performed F1-score of 97.44% in Massachusetts Building dataset and intersection over union as 80.75% in Inria dataset, higher than at least 3% than the previous studies.

Orijinal dilİngilizce
Makale numarası024510
DergiJournal of Applied Remote Sensing
Hacim16
Basın numarası2
DOI'lar
Yayın durumuYayınlandı - 1 Nis 2022

Bibliyografik not

Publisher Copyright:
© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).

Finansman

This study was supported by Istanbul Technical University Scientific Research Office (BAP) with the project number MDK-2018-41541. No potential conflict of interest was reported by the authors.

FinansörlerFinansör numarası
Istanbul Technical University Scientific Research Office
Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik ÜniversitesiMDK-2018-41541

    Parmak izi

    Comparative research on different backbone architectures of DeepLabV3+ for building segmentation' araştırma başlıklarına git. Birlikte benzersiz bir parmak izi oluştururlar.

    Alıntı Yap