Abstract
The semantic segmentation of high-resolution aerial images concerns the task of determining, for each pixel, the most likely class label from a finite set of possible labels (e.g., discriminating pixels referring to roads, buildings, or vegetation, in high-resolution images depicting urban areas). Following recent work in the area related to the use of fully-convolutional neural networks for semantic segmentation, we evaluated the performance of an adapted version of the W-Net architecture, which has achieved very good results on other types of image segmentation tasks. Through experiments with two distinct datasets frequently used in previous studies in the area, we show that the proposed W-Net architecture is quite effective in this task, outperforming a baseline corresponding to the U-Net model, and also some of the other recently proposed approaches.
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Acknowledgements
This research was supported through Fundação para a Ciência e Tecnologia (FCT), through the project grants with references PTDC/EEI-SCR/1743/2014 (Saturn), PTDC/CTA-OHR/29360/2017 (RiverCure), and PTDC/CCI-CIF/32607/2017 (MIMU), as well as through the INESC-ID multi-annual funding from the PIDDAC programme (UID/CEC/50021/2019). We also gratefully acknowledge the support of NVIDIA Corporation, with the donation of two Titan Xp GPUs used in the experiments reported on the paper.
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Dias, M., Monteiro, J., Estima, J., Silva, J., Martins, B. (2019). Semantic Segmentation of High-Resolution Aerial Imagery with W-Net Models. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_40
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