Ms. Lu Wang Profile

Lu Wang

at Qinghai Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 23 November 2022 Paper

Extraction of typical urban buildings in China based on K-Net

Zhongrong Xu, Lu Wang, Lei Zhang, Haiwen Chen, Lulu Cui, Shiying Wang

Proceedings Volume 12475, 1247516 (2022) https://doi.org/10.1117/12.2659698

KEYWORDS: Buildings, Remote sensing, Transformers, Image segmentation, Feature extraction, Data modeling, Vegetation, Image fusion, Convolution, Process modeling

Read Abstract +

The efficient extraction model of typical Chinese urban buildings is one of the important research contents to provide technical support for urban planning. However, the different architectural styles, a large number of vegetation and shadows in typical Chinese cities make the extraction of buildings with high errors. To address the problems of slow convergence speed and rough edge segmentation of the K-net combined with DeepLabv3 model on the remote sensing images of typical urban buildings in China, the Swin_ASPP_Knet network is proposed, which adds a multi-scale feature fusion module to enrich the feature information, and uses Swin Transformer to replace Resnet as the backbone network to extract more accurate feature information and accelerate the model convergence speed. After the comparative analysis of the experiments, the results show that the Swin_ASPP_Knet network outperforms K-Net combined with DeepLabv3 and Unet networks in the extraction task of typical urban building datasets in China, with mIoU and PA reaching 86.38% and 95.27%, respectively. To verify the generalization ability of the model, experiments were also conducted in the WHU building dataset where mIoU and PA reached 93.65% and 98.00%, respectively. Compared with the original model, the Swin_ASPP_Knet in both public building datasets reduced the model training time by half, and achieved good results in edge extraction of Chinese urban buildings with complex backgrounds, which met the requirement of efficient and accurate extraction of typical urban buildings in China.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years