Remote sensing images semantic segmentation method based on improved nested UNet

Zhongyu Li; Yang Liu; Yin Kuang; Huajun Wang; Cheng Liu

doi:10.1117/12.2667484

10 February 2023 Remote sensing images semantic segmentation method based on improved nested UNet

Zhongyu Li, Yang Liu, Yin Kuang, Huajun Wang, Cheng Liu

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 1255223 (2023) https://doi.org/10.1117/12.2667484
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

With the development of remote sensing technology, remote sensing images of buildings are of great significance in urban planning, disaster response, and other directions. When we use a neural network containing batch normalization layers for semantic segmentation, the neural network is sensitive to batch size and has low segmentation accuracy for occluded and dense buildings. This paper proposes a method for building segmentation in remote sensing images based on Nested UNet (UNet++) deep neural network. First, the UNet++ network is used to extract features, and the Group Normalization (GN) method is used instead of Batch Normalization (BN) to alleviate the model's sensitivity to batch size. Then, the weighted combination of Cross-Entropy Loss (CELoss) and DiceLoss is used as the loss function to improve the feature extraction ability of the neural network for unbalanced buildings. Finally, experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet++-GN) improves Mean Intersection over Union (MIoU) and Mean Pixel Accuracy (Macc) by 12.16% and 2.92%, respectively, compared with the original model (UNet++-BN).

Citation Download Citation

Zhongyu Li, Yang Liu, Yin Kuang, Huajun Wang, and Cheng Liu "Remote sensing images semantic segmentation method based on improved nested UNet", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 1255223 (10 February 2023); https://doi.org/10.1117/12.2667484

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