Dual Self-Attention Swin Transformer for Hyperspectral Image Super-Resolution

高光谱成像 计算机科学 人工智能 图像分辨率 遥感 模式识别(心理学) 卷积神经网络 计算机视觉 图像质量 图像(数学) 地质学
作者
Yaqian Long,Xun Wang,Meng Xu,Shuyu Zhang,Shuguo Jiang,Sen Jia
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-12 被引量:28
标识
DOI:10.1109/tgrs.2023.3275146
摘要

Spatial resolution is a crucial indicator for measuring the quality of hyperspectral imaging (HSI) and obtaining high-resolution (HR) hyperspectral images without any auxiliary information has become increasingly challenging. One promising approach is to use deep-learning (DL) techniques to reconstruct HR hyperspectral images from low-resolution (LR) images, namely super-resolution (SR). While convolutional neural networks are commonly used for hyperspectral image SR (HSI-SR), they often lead to unavoidable performance degradation due to the lack of long-range dependence learning ability. In this article, we propose a dual self-attention Swin transformer SR (DSSTSR) network that utilizes the ability of the shifted windows (Swin) transformer in the spatial representation of both global and local features and learns spectral sequence information from adjacent bands of HSI. Additionally, DSSTSR incorporates an image denoising module using the wavelet transformation method to mitigate the impact of stripe noise on HSI-SR. Our extensive experiments using publicly close-range datasets demonstrate that DSSTSR outperforms other state-of-art HSI-SR methods in terms of three image quality metrics. Furthermore, we applied DSSTSR to the SR of satellite hyperspectral images and achieved improved classification results. Compared to its competitors, DSSTSR exhibits superior performance in enhancing spatial resolution while preserving spectral information. These results suggest that the DSSTSR network has great potential for standardization in remote-sensing image processing and practical applications.
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
孙燕应助淳于安筠采纳,获得30
1秒前
2秒前
幸福大白发布了新的文献求助30
2秒前
wsj发布了新的文献求助10
4秒前
ZONG发布了新的文献求助20
4秒前
wuy发布了新的文献求助10
6秒前
7秒前
8秒前
8秒前
Jun关闭了Jun文献求助
8秒前
星星发布了新的文献求助10
9秒前
11秒前
射天狼发布了新的文献求助10
11秒前
11秒前
11秒前
zebra8848完成签到,获得积分10
11秒前
12秒前
深情安青应助wsj采纳,获得10
12秒前
12秒前
sxy发布了新的文献求助10
13秒前
蔡从安发布了新的文献求助10
14秒前
15秒前
柔弱云朵完成签到,获得积分10
16秒前
16秒前
16秒前
xxddw发布了新的文献求助10
16秒前
Owen应助小晓采纳,获得10
17秒前
18秒前
18秒前
18秒前
25秒前
25秒前
量子星尘发布了新的文献求助10
25秒前
25秒前
肖雪依完成签到,获得积分10
26秒前
28秒前
Tink完成签到,获得积分0
28秒前
麦子发布了新的文献求助10
31秒前
星辰大海应助自信鑫鹏采纳,获得10
32秒前
铮铮完成签到,获得积分10
32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