Method of Building Detection in Optical Remote Sensing Images Based on SegFormer

An appropriate detection network is required to extract building information in remote sensing images and to relieve the issue of poor detection effects resulting from the deficiency of detailed features. Firstly, we embed a transposed convolution sampling module fusing multiple normalization activa...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 3; s. 1258
Hlavní autoři: Li, Meilin, Rui, Jie, Yang, Songkun, Liu, Zhi, Ren, Liqiu, Ma, Li, Li, Qing, Su, Xu, Zuo, Xibing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 21.01.2023
MDPI
Témata:
atrous spatial pyramid
building detection
Buildings
Classification
machine vision
Methods
Neural networks
Remote sensing
remote sensing images
SegFormer
Semantics
SegFormer
remote sensing images
atrous spatial pyramid
building detection
machine vision
ISSN:1424-8220, 1424-8220
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Shrnutí:An appropriate detection network is required to extract building information in remote sensing images and to relieve the issue of poor detection effects resulting from the deficiency of detailed features. Firstly, we embed a transposed convolution sampling module fusing multiple normalization activation layers in the decoder based on the SegFormer network. This step alleviates the issue of missing feature semantics by adding holes and fillings, cascading multiple normalizations and activation layers to hold back over-fitting regularization expression and guarantee steady feature parameter classification. Secondly, the atrous spatial pyramid pooling decoding module is fused to explore multi-scale contextual information and to overcome issues such as the loss of detailed information on local buildings and the lack of long-distance information. Ablation experiments and comparison experiments are performed on the remote sensing image AISD, MBD, and WHU dataset. The robustness and validity of the improved mechanism are demonstrated by control groups of ablation experiments. In comparative experiments with the HRnet, PSPNet, U-Net, DeepLabv3+ networks, and the original detection algorithm, the mIoU of the AISD, the MBD, and the WHU dataset is enhanced by 17.68%, 30.44%, and 15.26%, respectively. The results of the experiments show that the method of this paper is superior to comparative methods such as U-Net. Furthermore, it is better for integrity detection of building edges and reduces the number of missing and false detections.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23031258