已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion

全色胶片 多光谱图像 计算机科学 高光谱成像 人工智能 图像分辨率 图像融合 模式识别(心理学) 特征(语言学) 计算机视觉 特征提取 遥感 图像(数学) 地理 语言学 哲学
作者
Kun Li,Wei Zhang,Dian Yu,Xin Tian
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing 卷期号:188: 30-44 被引量:32
标识
DOI:10.1016/j.isprsjprs.2022.04.001
摘要

Traditional approaches mainly fuse a hyperspectral image (HSI) with a high-resolution multispectral image (MSI) to improve the spatial resolution of the HSI. However, such improvement in the spatial resolution of HSIs is still limited because the spatial resolution of MSIs remains low. To further improve the spatial resolution of HSIs, we propose HyperNet, a deep network for the fusion of HSI, MSI, and panchromatic image (PAN), which effectively injects the spatial details of an MSI and a PAN into an HSI while preserving the spectral information of the HSI. Thus, we design HyperNet on the basis of a uniform fusion strategy to solve the problem of complex fusion of three types of sources (i.e., HSI, MSI, and PAN). In particular, the spatial details of the MSI and the PAN are extracted by multiple specially designed multiscale-attention-enhance blocks in which multi-scale convolution is used to adaptively extract features from different reception fields, and two attention mechanisms are adopted to enhance the representation capability of features along the spectral and spatial dimensions, respectively. Through the capability of feature reuse and interaction in a specially designed dense-detail-insertion block, the previously extracted features are subsequently injected into the HSI according to the unidirectional feature propagation among the layers of dense connection. Finally, we construct an efficient loss function by integrating the multi-scale structural similarity index with the L1 norm, which drives HyperNet to generate high-quality results with a good balance between spatial and spectral qualities. Extensive experiments on simulated and real data sets qualitatively and quantitatively demonstrate the superiority of HyperNet over other 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
SciGPT应助wukong采纳,获得10
2秒前
3秒前
6秒前
8秒前
10秒前
11秒前
Rain完成签到,获得积分10
11秒前
李爱国应助科研通管家采纳,获得10
13秒前
小马甲应助科研通管家采纳,获得10
13秒前
深情安青应助科研通管家采纳,获得10
13秒前
天天快乐应助科研通管家采纳,获得10
13秒前
彭于晏应助科研通管家采纳,获得10
13秒前
丘比特应助科研通管家采纳,获得10
13秒前
Rondab应助科研通管家采纳,获得30
13秒前
13秒前
FashionBoy应助科研通管家采纳,获得10
13秒前
15秒前
一直向前发布了新的文献求助10
16秒前
16秒前
可爱的函函应助mellow采纳,获得10
16秒前
19秒前
19秒前
TRY发布了新的文献求助10
20秒前
毕业论文三万字完成签到,获得积分10
21秒前
JOY发布了新的文献求助10
23秒前
momo发布了新的文献求助10
23秒前
晗晗有酒窝完成签到,获得积分10
23秒前
缥缈的碧曼完成签到,获得积分10
24秒前
斯文败类应助Steven采纳,获得10
25秒前
26秒前
27秒前
天天快乐应助大喵采纳,获得10
28秒前
mellow发布了新的文献求助10
30秒前
31秒前
31秒前
非也非也6发布了新的文献求助10
31秒前
JOY完成签到,获得积分10
32秒前
猫瓜西完成签到,获得积分10
32秒前
整齐凝竹完成签到 ,获得积分10
36秒前
37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 3989811
求助须知:如何正确求助?哪些是违规求助? 3531927
关于积分的说明 11255560
捐赠科研通 3270706
什么是DOI,文献DOI怎么找? 1805035
邀请新用户注册赠送积分活动 882181
科研通“疑难数据库(出版商)”最低求助积分说明 809190