Contrastive Mutual Learning with Pseudo-Label Smoothing for Hyperspectral Image Classification

高光谱成像 人工智能 平滑的 模式识别(心理学) 计算机科学 特征学习 相似性(几何) 机器学习 特征(语言学) 噪音(视频) 特征提取 图像(数学) 计算机视觉 语言学 哲学
作者
Liu Li-zhu,Hui Zhang,Yaonan Wang
出处
期刊:IEEE Transactions on Instrumentation and Measurement [Institute of Electrical and Electronics Engineers]
卷期号:73: 1-14 被引量:4
标识
DOI:10.1109/tim.2024.3406811
摘要

Semi-supervised learning has become an effective paradigm for reducing the reliance of hyperspectral image (HSI) classification on labeled data. State-of-the-art semi-supervised HSI classification methods learn supplementary knowledge from pseudo-labels, which are predicted by a deep learning model on unlabeled data. Nevertheless, these methods usually overlook the impacts of pseudo-label noise, intra-class spectral variability, and inter-class spectral similarity, which may fundamentally constrain the model's capability for refining feature representation. To address these prevalent issues, we propose a novel semi-supervised framework - contrastive mutual learning with pseudo-label smoothing (CMLP) to enable the model to learn more refined features. Firstly, we uniquely combine a mutual learning model and pseudo-label smoothing strategy to reduce the noise knowledge learned by the classification model during HSI feature extraction. Secondly, we incorporate a mutual pseudo-label guided contrastive learning approach, which helps to maximize interclass dispersion and minimize intraclass compactness, thus mitigating the problem of intra-class spectral variability and inter-class spectral similarity within HSI data. In addition, we have introduced a dynamic threshold strategy that adjusts the quantity of unlabeled samples introduced during the training process dynamically. This strategy mitigates the adverse impact from unstable predictions of unlabeled data in the early stages of training. The extensive experiments on three benchmark HSI datasets demonstrate that the proposed method can achieve competitive performance compared to state-of-the-art methods.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
3秒前
赘婿应助JJ采纳,获得10
3秒前
3秒前
xyj6486发布了新的文献求助10
4秒前
李BO完成签到 ,获得积分10
5秒前
6秒前
7秒前
SciGPT应助Xixicccccccc采纳,获得10
7秒前
7秒前
heavenzzz发布了新的文献求助10
8秒前
所所应助大反应釜采纳,获得10
8秒前
Orange应助吴所谓采纳,获得50
9秒前
9秒前
无知小白完成签到,获得积分10
9秒前
moumou完成签到,获得积分10
10秒前
李钧鹏完成签到,获得积分10
10秒前
laber应助小诗人采纳,获得50
11秒前
有魅力的不评完成签到,获得积分10
11秒前
麋鹿发布了新的文献求助10
11秒前
13秒前
火星上含海完成签到,获得积分10
15秒前
小马甲应助张雯思采纳,获得10
19秒前
41应助张雯思采纳,获得10
19秒前
李健的粉丝团团长应助123采纳,获得10
19秒前
孙燕应助张雯思采纳,获得10
19秒前
打打应助张雯思采纳,获得10
19秒前
情怀应助张雯思采纳,获得10
19秒前
孙燕应助张雯思采纳,获得10
19秒前
Hello应助张雯思采纳,获得10
19秒前
搜集达人应助张雯思采纳,获得10
19秒前
赘婿应助张雯思采纳,获得10
19秒前
今后应助张雯思采纳,获得10
19秒前
量子星尘发布了新的文献求助10
20秒前
麋鹿完成签到,获得积分20
20秒前
赵子完成签到,获得积分10
23秒前
英姑应助会飞的鱼采纳,获得10
24秒前
27秒前
FashionBoy应助勤恳化蛹采纳,获得10
28秒前
30秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989444
求助须知:如何正确求助?哪些是违规求助? 3531531
关于积分的说明 11254250
捐赠科研通 3270191
什么是DOI,文献DOI怎么找? 1804901
邀请新用户注册赠送积分活动 882105
科研通“疑难数据库(出版商)”最低求助积分说明 809174