Remote Sensing Building Detection Based on Binarized Semantic Segmentation

Tianyou, 朱天佑 Zhu; Feng, 董峰 Dong; Huixing, 龚惠兴 Gong

doi:10.3788/aos201939.1228002

Cited by 2 publications

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“…With the rise of artificial intelligence technology, convolutional neural networks can effectively remove the interference between the ground object information of remote sensing images and effectively extract target features, so they are widely used in the field of remote sensing image building semantic segmentation. Zhu Tianyou [4] et al proposed a semantic segmentation network MBU-Net based on the mixed-use of binary and floating-point numbers to detect buildings in remote sensing images in real-time. Liu Hao [5] et al improved the UNet network and used the combination of Dice coefficient and cross-entropy function as the loss function, and achieved good results in the extraction of irregular buildings.…”

Section: Introductionmentioning

confidence: 99%

A semantic segmentation method of buildings in remote sensing image based on improved UNet

Liu²,

Yin³

et al. 2022

2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022)

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Aiming at the problem of model instability and overfitting of deep neural networks with the deepening of the number of network layers, the current mainstream method is to use batch normalization (BN) to alleviate them. However, since the BN method is more sensitive to batch size when the batch size is small, the model performance will be poor. For a relatively large model, due to the limitation of video memory, the batch size cannot take a large value, limiting the model's performance. Because of the dependence of BN on batch size, this paper adopts group normalization (GN) instead of batch normalization (BN) in the UNet network to alleviate the impact of the model on batch size. Then experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet-GN) improves the mean intersection over union (MIoU) and mean pixel accuracy (MPA) by 10.66% and 1.65% respectively compared with the original model (UNet-BN).

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Section: Introductionmentioning

confidence: 99%

A semantic segmentation method of buildings in remote sensing image based on improved UNet

Liu²,

Yin³

et al. 2022

2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022)

View full text Add to dashboard Cite

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Full Convolutional Neural Network Based on Multi-Scale Feature Fusion for the Class Imbalance Remote Sensing Image Classification

Ren

Zhang

et al. 2020

Remote Sensing

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Remote sensing image segmentation with samples imbalance is always one of the most important issues. Typically, a high-resolution remote sensing image has the characteristics of high spatial resolution and low spectral resolution, complex large-scale land covers, small class differences for some land covers, vague foreground, and imbalanced distribution of samples. However, traditional machine learning algorithms have limitations in deep image feature extraction and dealing with sample imbalance issue. In the paper, we proposed an improved full-convolution neural network, called DeepLab V3+, with loss function based solution of samples imbalance. In addition, we select Sentinel-2 remote sensing images covering the Yuli County, Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China as data sources, then a typical region image dataset is built by data augmentation. The experimental results show that the improved DeepLab V3+ model can not only utilize the spectral information of high-resolution remote sensing images, but also consider its rich spatial information. The classification accuracy of the proposed method on the test dataset reaches 97.97%. The mean Intersection-over-Union reaches 87.74%, and the Kappa coefficient 0.9587. The work provides methodological guidance to sample imbalance correction, and the established data resource can be a reference to further study in the future.

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Remote Sensing Building Detection Based on Binarized Semantic Segmentation

Cited by 2 publications

References 0 publications

A semantic segmentation method of buildings in remote sensing image based on improved UNet

A semantic segmentation method of buildings in remote sensing image based on improved UNet

Full Convolutional Neural Network Based on Multi-Scale Feature Fusion for the Class Imbalance Remote Sensing Image Classification

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