Pansharpening Using Unsupervised Generative Adversarial Networks With Recursive Mixed-Scale Feature Fusion

计算机科学 全色胶片 人工智能 特征(语言学) 多光谱图像 模式识别(心理学) 比例(比率) 特征提取 融合机制 图像分辨率 数据挖掘 融合 哲学 物理 脂质双层融合 量子力学 语言学
作者
Yuanyuan Wu,Yuchun Li,Siling Feng,Mengxing Huang
出处
期刊:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:16: 3742-3759 被引量:14
标识
DOI:10.1109/jstars.2023.3259014
摘要

Panchromatic sharpening (pansharpening) is an important technology for improving the spatial resolution of multispectral (MS) images. The majority of the models are implemented at the reduced resolution, leading to unfavorable results at the full resolution. Moreover, the complicated relationship between MS and panchromatic (PAN) images is often ignored in detail injection. For the mentioned problems, unsupervised generative adversarial networks with recursive mixed-scale feature fusion for pansharpening (RMFF-UPGAN) are modeled to boost the spatial resolution and preserve the spectral information. RMFF-UPGAN comprises a generator and two U-shaped discriminators. A dual-stream trapezoidal branch is designed in the generator to obtain multiscale information. Further, a recursive mixed-scale feature fusion subnetwork is designed. Perform a prior fusion on the extracted MS and PAN features of the same scale. A mixed-scale fusion is conducted on the prior fusion results of the fine-scale and coarse-scale. The fusion is executed sequentially in the above manner building a recursive mixed-scale fusion structure and finally generating key information. A compensation information mechanism is also designed for the reconstruction of key information to compensate for information. A nonlinear rectification block for the reconstructed information is developed to overcome the distortion induced by neglecting the complicated relationship between MS and PAN images. Two U-shaped discriminators are designed and a new composite loss function is defined. The presented model is validated using two satellite data and the outcomes reveal better than the prevalent approaches regarding both visual assessment and objective indicators.

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
1秒前
1秒前
天空中飞翔的鱼完成签到,获得积分10
2秒前
认真的觅松完成签到 ,获得积分10
3秒前
3秒前
3秒前
雪上一枝蒿完成签到,获得积分10
4秒前
淡然绝山发布了新的文献求助10
4秒前
云栖完成签到,获得积分10
4秒前
酷波er应助antman采纳,获得10
4秒前
hyhy发布了新的文献求助10
6秒前
云栖发布了新的文献求助10
7秒前
文静发布了新的文献求助10
8秒前
米尔的猫发布了新的文献求助10
8秒前
8秒前
桐桐应助简单男孩采纳,获得10
8秒前
学术laji完成签到 ,获得积分10
9秒前
9秒前
碧蓝丹烟完成签到 ,获得积分10
10秒前
小学教材全解完成签到,获得积分10
11秒前
今后应助loomcool采纳,获得30
12秒前
科目三应助开心苠采纳,获得10
12秒前
李李李李李完成签到,获得积分10
13秒前
小星云完成签到,获得积分10
13秒前
14秒前
Rainie发布了新的文献求助50
14秒前
sddq发布了新的文献求助10
14秒前
啦啦啦喽完成签到,获得积分10
15秒前
CMC完成签到,获得积分10
16秒前
jackhlj完成签到,获得积分10
18秒前
1111完成签到,获得积分10
19秒前
qq应助淡然绝山采纳,获得10
19秒前
dypdyp应助云栖采纳,获得10
19秒前
隐形曼青应助senpaiser采纳,获得10
20秒前
20秒前
shenhang23发布了新的文献求助50
21秒前
alexstu完成签到,获得积分10
21秒前
Denmark发布了新的文献求助50
21秒前
asdfqwer应助科研通管家采纳,获得10
22秒前
脆脆完成签到,获得积分10
22秒前
高分求助中
Ophthalmic Equipment Market by Devices(surgical: vitreorentinal,IOLs,OVDs,contact lens,RGP lens,backflush,diagnostic&monitoring:OCT,actorefractor,keratometer,tonometer,ophthalmoscpe,OVD), End User,Buying Criteria-Global Forecast to2029 2000
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
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 3963699
求助须知:如何正确求助?哪些是违规求助? 3509612
关于积分的说明 11147847
捐赠科研通 3243109
什么是DOI,文献DOI怎么找? 1792047
邀请新用户注册赠送积分活动 873390
科研通“疑难数据库(出版商)”最低求助积分说明 803788