TY  - JOUR
TI  - Comparison of Different U-Net Models for Building Extraction from High-Resolution Aerial Imagery
AB  - Building extraction from high-resolution aerial imagery plays an important role in geospatial applications such as urban planning,telecommunication, disaster monitoring, navigation, updating geographic databases, and urban dynamic monitoring. Automaticbuilding extraction is a challenging task, as the buildings in different regions have different spectral and geometric properties.Therefore, the classical image processing techniques are not sufficient for automatic building extraction from high-resolution aerialimagery applications. Deep learning and semantic segmentation models, which have gained popularity in recent years, have beenused for automatic object extraction from high-resolution images. U-Net model, which was originally developed for biomedicalimage processing, was used for building extraction. The encoder part of the U-Net model has been modified with Vgg16,InceptionResNetV2, and DenseNet121 convolutional neural networks. Therefore, building extraction was performed using Vgg16 UNet, InceptionResNetV2 U-Net, and DenseNet121 U-Net models. In the fourth method, the results obtained from each U-Net modelwere combined in order to obtain the final result by maximum voting. This study aims to compare the performance of these fourmethods in building extraction from high-resolution aerial imagery. Images of Chicago from the Inria Aerial Image Labeling Datasetwere used in the study. The images used have 0.3 m spatial resolution, 8-bit radiometric resolution, and 3-band (red, green, and bluebands). Images consist of 36 tiles and they were divided into image subsets of 512x512 pixels. Thus, a total of 2715 image subsetswere formed. 80% of the image subsets (2172 image subset) were used as training and 20% (543 image subset) as testing. Toevaluate the accuracy of methods, the F1 score of the building class was employed. The F1 scores for building class have beencalculated as 0.866, 0.860, 0.856, and 0.877 on test images for U-Net Vgg16, U-Net InceptionResNetV2, U-Net DenseNet121, andmajority voting method, respectively.
AU  - Erdem, Fırat
AU  - Avdan, Ugur
DO  - 10.30897/ijegeo.684951
PY  - 2020
JO  - International Journal of Environment and Geoinformatics
VL  - 7
IS  - 3
SN  - 2148-9173
SP  - 221
EP  - 227
DB  - TRDizin
UR  - http://search/yayin/detay/429263
ER  -