Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba
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, th...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/19/3622 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850284522430529536 |
|---|---|
| author | Qi Zhang Guohua Geng Pengbo Zhou Qinglin Liu Yong Wang Kang Li |
| author_facet | Qi Zhang Guohua Geng Pengbo Zhou Qinglin Liu Yong Wang Kang Li |
| author_sort | Qi Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-10e508a2a220490d9fbb5140af3cb52e |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-10e508a2a220490d9fbb5140af3cb52e2025-08-20T01:47:33ZengMDPI AGRemote Sensing2072-42922024-09-011619362210.3390/rs16193622Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation MambaQi Zhang0Guohua Geng1Pengbo Zhou2Qinglin Liu3Yong Wang4Kang Li5School of Information Science and Technology, Northwest University, Xi’an 710127, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaSchool of Arts and Communication, Beijing Normal University, Beijing 100875, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaThe 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.https://www.mdpi.com/2072-4292/16/19/3622semantic segmentationremote sensingMambastate-space modellink aggregation |
| spellingShingle | Qi Zhang Guohua Geng Pengbo Zhou Qinglin Liu Yong Wang Kang Li Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba Remote Sensing semantic segmentation remote sensing Mamba state-space model link aggregation |
| title | Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba |
| title_full | Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba |
| title_fullStr | Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba |
| title_full_unstemmed | Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba |
| title_short | Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba |
| title_sort | link aggregation for skip connection mamba remote sensing image segmentation network based on link aggregation mamba |
| topic | semantic segmentation remote sensing Mamba state-space model link aggregation |
| url | https://www.mdpi.com/2072-4292/16/19/3622 |
| work_keys_str_mv | AT qizhang linkaggregationforskipconnectionmambaremotesensingimagesegmentationnetworkbasedonlinkaggregationmamba AT guohuageng linkaggregationforskipconnectionmambaremotesensingimagesegmentationnetworkbasedonlinkaggregationmamba AT pengbozhou linkaggregationforskipconnectionmambaremotesensingimagesegmentationnetworkbasedonlinkaggregationmamba AT qinglinliu linkaggregationforskipconnectionmambaremotesensingimagesegmentationnetworkbasedonlinkaggregationmamba AT yongwang linkaggregationforskipconnectionmambaremotesensingimagesegmentationnetworkbasedonlinkaggregationmamba AT kangli linkaggregationforskipconnectionmambaremotesensingimagesegmentationnetworkbasedonlinkaggregationmamba |