A segmentation method for building in remote sensing image based on deep learning

Riya Su; Yuan Ma; Xudong Wang

doi:10.1117/12.2640812

13 October 2022 A segmentation method for building in remote sensing image based on deep learning

Riya Su, Yuan Ma, Xudong Wang

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228729 (2022) https://doi.org/10.1117/12.2640812
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Rapid and automatic extraction of buildings from remote sensing images can provide an auxiliary decision-making basis for urban management, military investigation, and post-disaster emergency assessment. Based on the deep learning model UNet++, this paper integrates the EfficientNet and SGE attention, combined with the auxiliary training of crossentropy loss function, so that the model can segment the buildings in remote sensing images. The experimental results on the Inria Aerial Image Labeling dataset show that the evaluation indexes of the proposed model are high in five cities, with the highest OA of 96.83%, mIoU of 0.80, and Kappa of 0.76. The buildings in the segmentation results are complete and accurate, and the boundary is clear.

Citation Download Citation

Riya Su, Yuan Ma, and Xudong Wang "A segmentation method for building in remote sensing image based on deep learning", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228729 (13 October 2022); https://doi.org/10.1117/12.2640812

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KEYWORDS

Image segmentation

Remote sensing

Data modeling

Earthquakes

Eye models

Feature extraction

Image processing

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