Remote sensing landslide recognition method based on LinkNet and attention mechanism

Jing Yang; Yaohua Luo; Xuben Wang; Haoyu Tang; Shanshan Rao

doi:10.1117/12.2667640

28 March 2023 Remote sensing landslide recognition method based on LinkNet and attention mechanism

Jing Yang, Yaohua Luo, Xuben Wang, Haoyu Tang, Shanshan Rao

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125662M (2023) https://doi.org/10.1117/12.2667640
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

Rapid detection and identification of landslide areas are very important for disaster prevention and mitigation. Aiming at the problems of time-consuming and labor-intensive traditional landslide information extraction methods and low recognition efficiency, a remote sensing landslide recognition method based on LinkNet, and convolution attention module was proposed. The model adopts the coding-decoding structure to improve the operation efficiency. The Convolutional Block Attention Module (CBAM) is applied to optimize the weight allocation from both channel and spatial dimensions to highlight the landslide feature information. And compared with the traditional U-Net and LinkNet models. The results show that the CBAM-LinkNet model has excellent performance in remote sensing landslide identification, which provides the possibility for rapid and accurate landslide identification.

Citation Download Citation

Jing Yang, Yaohua Luo, Xuben Wang, Haoyu Tang, and Shanshan Rao "Remote sensing landslide recognition method based on LinkNet and attention mechanism", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125662M (28 March 2023); https://doi.org/10.1117/12.2667640

ACCESS THE FULL ARTICLE

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