ECAB-SegFormer: A Boundary-Aware and Efficient Channel Attention Network for Ulva prolifera Semantic Segmentation in Remote Sensing Imagery

Authors

Shanzhe Zhang (ORCID: https://orcid.org/0000-0003-2299-0386)
Danyang Cao (ORCID: https://orcid.org/0009-0006-2239-9456)
H. J. Yang (ORCID: https://orcid.org/0009-0004-8274-9863)
Yulong Song
Yue Liang
Zice Ji
Maohua Guo
Xutong Guo
Xiaoya Liu

Institutions

North China University of Technology (CN)
Beijing Technology and Business University (CN)
Ministry of Natural Resources (CN)
Beijing Academy of Artificial Intelligence (CN)
National Satellite Ocean Application Service (CN)
State Key Laboratory of Remote Sensing Science (CN)

Publication Details

Journal: Sensors
Published: 2026-03-31
DOI: https://doi.org/10.3390/s26072166
Primary Topic: Advanced Neural Network Applications
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Natural Science Foundation of Beijing Municipality
North China University of Technology
National Key Research and Development Program of China

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article

ECAB-SegFormer: A Boundary-Aware and Efficient Channel Attention Network for Ulva prolifera Semantic Segmentation in Remote Sensing Imagery

Shanzhe Zhang, Danyang Cao, H. J. Yang, Yulong Song et al.

Mar 31, 2026

Sensors

Advanced Neural Network Applications

article

ECAB-SegFormer: A Boundary-Aware and Efficient Channel Attention Network for Ulva prolifera Semantic Segmentation in Remote Sensing Imagery

Shanzhe Zhang, Danyang Cao, H. J. Yang, Yulong Song, Yue Liang, Zice Ji, Maohua Guo, Xutong Guo, Xiaoya Liu

March 31, 2026article en

Abstract

No abstract available for this paper.

SensorsVol. 26(7)

North China University of Technology (CN), Beijing Technology and Business University (CN), Ministry of Natural Resources (CN), Beijing Academy of Artificial Intelligence (CN), National Satellite Ocean Application Service (CN), State Key Laboratory of Remote Sensing Science (CN)

Natural Science Foundation of Beijing Municipality, North China University of Technology, National Key Research and Development Program of China

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Openalex Percentile: Top 37%

Advanced Neural Network Applications

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