TCUNet: A Lightweight Dual-Branch Parallel Network for Sea–Land Segmentation in Remote Sensing Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 18; p. 4413
• Main Authors: Xiong, Xuan, Wang, Xiaopeng, Zhang, Jiahua, Huang, Baoxiang, Du, Runfeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Artificial neural networks; Classification; CNN; Coders; Computer applications; Datasets; Deep learning; double-branch; Ecosystem components; Ecosystem structure; Erosion rates; Feature extraction; GF-6; Hydrology; Image processing; Image segmentation; Imports; Machine learning; Medical imaging equipment; Modules; Neural networks; Remote sensing; sea–land segmentation; Semantic segmentation; Semantics; Shoreline protection; Shorelines; transformer; Transformers

Remote sensing techniques for shoreline extraction are crucial for monitoring changes in erosion rates, surface hydrology, and ecosystem structure. In recent years, Convolutional neural networks (CNNs) have developed as a cutting-edge deep learning technique that has been extensively used in shoreli...