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Open AccessArticle
Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba
by
Qi Zhang
Qi Zhang 1,
Guohua Geng
Guohua Geng 1,*,
Pengbo Zhou
Pengbo Zhou 2,
Qinglin Liu
Qinglin Liu 1,
Yong Wang
Yong Wang 1 and
Kang Li
Kang Li 1
1
School of Information Science and Technology, Northwest University, Xi’an 710127, China
2
School of Arts and Communication, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(19), 3622; https://doi.org/10.3390/rs16193622 (registering DOI)
Submission received: 12 June 2024
/
Revised: 5 September 2024
/
Accepted: 13 September 2024
/
Published: 28 September 2024
Abstract
The semantic segmentation of satellite and UAV remote sensing imagery is pivotal for address exploration, change detection, quantitative analysis and urban planning. Recent advancements have seen an influx of segmentation networks utilizing convolutional neural networks and transformers. However, the intricate geographical features and varied land cover boundary interferences in remote sensing imagery still challenge conventional segmentation networks’ spatial representation and long-range dependency capabilities. This paper introduces a novel U-Net-like network for UAV image segmentation. We developed a link aggregation Mamba at the critical skip connection stage of UNetFormer. This approach maps and aggregates multi-scale features from different stages into a unified linear dimension through four Mamba branches containing state-space models (SSMs), ultimately decoupling and fusing these features to restore the contextual relationships in the mask. Moreover, the Mix-Mamba module is incorporated, leveraging a parallel self-attention mechanism with SSMs to merge the advantages of a global receptive field and reduce modeling complexity. This module facilitates nonlinear modeling across different channels and spaces through multipath activation, catering to international and local long-range dependencies. Evaluations on public remote sensing datasets like LovaDA, UAVid and Vaihingen underscore the state-of-the-art performance of our approach.
Share and Cite
MDPI and ACS Style
Zhang, Q.; Geng, G.; Zhou, P.; Liu, Q.; Wang, Y.; Li, K.
Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba. Remote Sens. 2024, 16, 3622.
https://doi.org/10.3390/rs16193622
AMA Style
Zhang Q, Geng G, Zhou P, Liu Q, Wang Y, Li K.
Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba. Remote Sensing. 2024; 16(19):3622.
https://doi.org/10.3390/rs16193622
Chicago/Turabian Style
Zhang, Qi, Guohua Geng, Pengbo Zhou, Qinglin Liu, Yong Wang, and Kang Li.
2024. "Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba" Remote Sensing 16, no. 19: 3622.
https://doi.org/10.3390/rs16193622
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