P2Sharpen: A progressive pansharpening network with deep spectral transformation

计算机科学 全色胶片 人工智能 转化(遗传学) 锐化 图像分辨率 模式识别(心理学) 一致性(知识库) 基本事实 计算机视觉 生物化学 基因 化学
作者
Hao Zhang,Hebaixu Wang,Xin Tian,Jiayi Ma
出处
期刊:Information Fusion [Elsevier]
卷期号:91: 103-122 被引量:19
标识
DOI:10.1016/j.inffus.2022.10.010
摘要

Most existing deep learning-based methods for pansharpening task solely rely on the supervision of pseudo-ground-truth multi-spectral images, which exhibits two limitations in producing high-quality images. On the one hand, it is uncontrollable to regulate the full-resolution performance due to the fact that their whole training process only remain at the scale of reduced resolution. On the other hand, they ignore the accurate spatial information reference of high-resolution panchromatic images for supervision, resulting in insufficient spatial structure details. To address these challenges, we propose a progressive pansharpening network with deep spectral transformation, termed as P2Sharpen, where we balance the performance in different resolutions and make full use of the observed satellite data to improve the quality of fused results. First, we design a spectral transformation network (STNet) to cross the modality difference between multi-spectral data and panchromatic data, which establishes an accurate mapping function from MS to PAN images. Second, we propose a progressive pansharpening network (P2Net), in which the optimization of pansharpening at reduced and full resolutions is considered in a two-stage manner, balancing the performance at two scales effectively. In addition, we introduce the trained STNet to construct the consistency constraint between the sharpened result and PAN image at both reduced-resolution stage and full-resolution stage, which further improves the ability of P2Net for preserving spatial textures. Extensive experiments demonstrate that our method shows excellent performance over the state-of-the-arts on the sharpening quality and the spectral response consistency in both reduced and full resolutions. Moreover, the proposed method can be applied to generate the high-resolution normalized difference vegetation index with promising accuracy.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
维维完成签到 ,获得积分10
1秒前
YJ完成签到,获得积分10
1秒前
victory_liu完成签到,获得积分10
5秒前
Herbs完成签到 ,获得积分10
9秒前
Jonsnow完成签到 ,获得积分10
11秒前
孤独的大灰狼完成签到 ,获得积分10
11秒前
马登完成签到,获得积分10
19秒前
liguanyu1078完成签到,获得积分10
21秒前
SciGPT应助FiroZhang采纳,获得10
23秒前
chenbin完成签到,获得积分10
25秒前
陈米花完成签到,获得积分10
25秒前
yyjl31完成签到,获得积分10
25秒前
Simon_chat完成签到,获得积分10
26秒前
路路完成签到 ,获得积分10
26秒前
Aprilzhou完成签到,获得积分10
31秒前
吐司炸弹完成签到,获得积分10
32秒前
mayfly完成签到,获得积分10
32秒前
轻松思枫完成签到 ,获得积分10
43秒前
gms完成签到,获得积分10
48秒前
Sunny完成签到 ,获得积分10
1分钟前
CHANG完成签到 ,获得积分10
1分钟前
sunnyqqz完成签到,获得积分10
1分钟前
慕子完成签到 ,获得积分10
1分钟前
葶ting完成签到 ,获得积分10
1分钟前
xianyaoz完成签到 ,获得积分10
1分钟前
薏仁完成签到 ,获得积分10
1分钟前
科研通AI2S应助吧嗒嗒采纳,获得10
1分钟前
FashionBoy应助55555采纳,获得10
1分钟前
汉堡包应助潇潇雨歇采纳,获得10
1分钟前
liuyong6413完成签到 ,获得积分10
1分钟前
顾矜应助潇潇雨歇采纳,获得10
2分钟前
2分钟前
踏实的书包完成签到,获得积分10
2分钟前
55555发布了新的文献求助10
2分钟前
飘飘完成签到,获得积分10
2分钟前
安静严青完成签到 ,获得积分10
2分钟前
陶醉的翠霜完成签到 ,获得积分10
2分钟前
zyb完成签到 ,获得积分10
2分钟前
bluelemon完成签到,获得积分10
2分钟前
xue112完成签到 ,获得积分10
2分钟前
高分求助中
좌파는 어떻게 좌파가 됐나:한국 급진노동운동의 형성과 궤적 2500
Sustainability in Tides Chemistry 1500
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
Cognitive linguistics critical concepts in linguistics 800
Threaded Harmony: A Sustainable Approach to Fashion 799
Livre et militantisme : La Cité éditeur 1958-1967 500
氟盐冷却高温堆非能动余热排出性能及安全分析研究 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3052652
求助须知:如何正确求助?哪些是违规求助? 2709863
关于积分的说明 7418267
捐赠科研通 2354446
什么是DOI,文献DOI怎么找? 1246020
科研通“疑难数据库(出版商)”最低求助积分说明 605951
版权声明 595921