Performance evaluation of shallow and deep CNN architectures on building segmentation from high-resolution images

Sarıtürk, Batuhan; Şeker, Dursun Zafer; Öztürk, Ozan; Bayram, Bülent

doi:10.1007/s12145-022-00840-5

Cited by 6 publications

(1 citation statement)

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“…Furthermore, numerous studies have been published to enhance the building segmentation performance of the models by concentrating on the network architectural design. Along with the attention mechanisms, approaches such as deep and shallow networks [ 32 ], multiple receptive fields, and residual connections [ 33 ] have been widely used.…”

Section: Introductionmentioning

confidence: 99%

A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images

Sarıtürk

Şeker

2022

Sensors

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Building segmentation is crucial for applications extending from map production to urban planning. Nowadays, it is still a challenge due to CNNs’ inability to model global context and Transformers’ high memory need. In this study, 10 CNN and Transformer models were generated, and comparisons were realized. Alongside our proposed Residual-Inception U-Net (RIU-Net), U-Net, Residual U-Net, and Attention Residual U-Net, four CNN architectures (Inception, Inception-ResNet, Xception, and MobileNet) were implemented as encoders to U-Net-based models. Lastly, two Transformer-based approaches (Trans U-Net and Swin U-Net) were also used. Massachusetts Buildings Dataset and Inria Aerial Image Labeling Dataset were used for training and evaluation. On Inria dataset, RIU-Net achieved the highest IoU score, F1 score, and test accuracy, with 0.6736, 0.7868, and 92.23%, respectively. On Massachusetts Small dataset, Attention Residual U-Net achieved the highest IoU and F1 scores, with 0.6218 and 0.7606, and Trans U-Net reached the highest test accuracy, with 94.26%. On Massachusetts Large dataset, Residual U-Net accomplished the highest IoU and F1 scores, with 0.6165 and 0.7565, and Attention Residual U-Net attained the highest test accuracy, with 93.81%. The results showed that RIU-Net was significantly successful on Inria dataset. On Massachusetts datasets, Residual U-Net, Attention Residual U-Net, and Trans U-Net provided successful results.

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Section: Introductionmentioning

confidence: 99%