利用自监督门控多模式转换器进行多标签遥感分类。

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-09-24 eCollection Date: 2024-01-01 DOI:10.3389/fncom.2024.1404623
Na Liu, Ye Yuan, Guodong Wu, Sai Zhang, Jie Leng, Lihong Wan
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引用次数: 0

摘要

导言:随着变形金刚在机器学习领域的巨大成功,它也逐渐引起了遥感(RS)领域的广泛关注。然而,遥感领域的研究一直受制于缺乏大型标注数据集以及遥感平台多样性导致的数据模式不一致。近年来,随着自监督学习(SSL)算法的兴起,RS 研究人员开始关注 "预训练和微调 "范式在 RS 中的应用。然而,遥感领域的多模态数据融合研究还很少。他们大多选择只使用其中一种模态数据或简单地将多种模态数据粗略拼接的方法:为了研究一种更有效的多模态数据融合方案,我们提出了一种基于门控单元控制的多模态融合机制(MGSViT)。本文结合两种常用的 SSL 算法,基于 BigEarthNet 数据集对 ViT 模型进行预训练,并结合多光谱(MS)和合成孔径雷达(SAR),提出了用于特征学习的模内和模间门控融合单元。我们的方法可以有效地结合不同模态数据来提取关键特征信息:经过微调和对比实验,我们在所有下游分类任务中的表现都优于最先进的算法。我们提出的方法的有效性得到了验证。
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Multi-label remote sensing classification with self-supervised gated multi-modal transformers.

Introduction: With the great success of Transformers in the field of machine learning, it is also gradually attracting widespread interest in the field of remote sensing (RS). However, the research in the field of remote sensing has been hampered by the lack of large labeled data sets and the inconsistency of data modes caused by the diversity of RS platforms. With the rise of self-supervised learning (SSL) algorithms in recent years, RS researchers began to pay attention to the application of "pre-training and fine-tuning" paradigm in RS. However, there are few researches on multi-modal data fusion in remote sensing field. Most of them choose to use only one of the modal data or simply splice multiple modal data roughly.

Method: In order to study a more efficient multi-modal data fusion scheme, we propose a multi-modal fusion mechanism based on gated unit control (MGSViT). In this paper, we pretrain the ViT model based on BigEarthNet dataset by combining two commonly used SSL algorithms, and propose an intra-modal and inter-modal gated fusion unit for feature learning by combining multispectral (MS) and synthetic aperture radar (SAR). Our method can effectively combine different modal data to extract key feature information.

Results and discussion: After fine-tuning and comparison experiments, we outperform the most advanced algorithms in all downstream classification tasks. The validity of our proposed method is verified.

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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
期刊最新文献
Editorial: Computational modeling and machine learning methods in neurodevelopment and neurodegeneration: from basic research to clinical applications. Simulated synapse loss induces depression-like behaviors in deep reinforcement learning. Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability. Facial emotion recognition using deep quantum and advanced transfer learning mechanism. BrainNet: an automated approach for brain stress prediction utilizing electrodermal activity signal with XLNet model.
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