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Open AccessArticle
Semantic Segmentation of Urban Remote Sensing Images Based on Deep Learning
by
Jingyi Liu
Jingyi Liu 1,
Jiawei Wu
Jiawei Wu 1,
Hongfei Xie
Hongfei Xie 2,
Dong Xiao
Dong Xiao 2,* and
Mengying Ran
Mengying Ran 2
1
College of Sciences, Northeastern University, Shenyang 110819, China
2
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(17), 7499; https://doi.org/10.3390/app14177499 (registering DOI)
Submission received: 19 July 2024
/
Revised: 17 August 2024
/
Accepted: 22 August 2024
/
Published: 24 August 2024
Abstract
In the realm of urban planning and environmental evaluation, the delineation and categorization of land types are pivotal. This study introduces a convolutional neural network-based image semantic segmentation approach to delineate parcel data in remote sensing imagery. The initial phase involved a comparative analysis of various CNN architectures. ResNet and VGG serve as the foundational networks for training, followed by a comparative assessment of the experimental outcomes. Subsequently, the VGG+U-Net model, which demonstrated superior efficacy, was chosen as the primary network. Enhancements to this model were made by integrating attention mechanisms. Specifically, three distinct attention mechanisms—spatial, SE, and channel—were incorporated into the VGG+U-Net framework, and various loss functions were evaluated and selected. The impact of these attention mechanisms, in conjunction with different loss functions, was scrutinized. This study proposes a novel network model, designated VGG+U-Net+Channel, that leverages the VGG architecture as the backbone network in conjunction with the U-Net structure and augments it with the channel attention mechanism to refine the model’s performance. This refinement resulted in a 1.14% enhancement in the network’s overall precision and marked improvements in MPA and MioU. A comparative analysis of the detection capabilities between the enhanced and original models was conducted, including a pixel count for each category to ascertain the extent of various semantic information. The experimental validation confirms the viability and efficacy of the proposed methodology.
Share and Cite
MDPI and ACS Style
Liu, J.; Wu, J.; Xie, H.; Xiao, D.; Ran, M.
Semantic Segmentation of Urban Remote Sensing Images Based on Deep Learning. Appl. Sci. 2024, 14, 7499.
https://doi.org/10.3390/app14177499
AMA Style
Liu J, Wu J, Xie H, Xiao D, Ran M.
Semantic Segmentation of Urban Remote Sensing Images Based on Deep Learning. Applied Sciences. 2024; 14(17):7499.
https://doi.org/10.3390/app14177499
Chicago/Turabian Style
Liu, Jingyi, Jiawei Wu, Hongfei Xie, Dong Xiao, and Mengying Ran.
2024. "Semantic Segmentation of Urban Remote Sensing Images Based on Deep Learning" Applied Sciences 14, no. 17: 7499.
https://doi.org/10.3390/app14177499
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