STFDiff: Remote sensing image spatiotemporal fusion with diffusion models

计算机科学 图像融合 扩散 融合 遥感 图像(数学) 计算机视觉 人工智能 地质学 物理 语言学 热力学 哲学
作者
He Huang,Wei He,Hongyan Zhang,Yu Xia,Liangpei Zhang
出处
期刊:Information Fusion [Elsevier BV]
卷期号:111: 102505-102505 被引量:6
标识
DOI:10.1016/j.inffus.2024.102505
摘要

Spatiotemporal fusion (STF) methods aim to blend satellite images with different spatial and temporal resolutions to support more frequent and precise monitoring. In the past decades, amounts of STF methods have been developed with remarkable success. However, among the existing methods, the traditional methods rely on the linear assumption and fail for complex and diverse scenes with great dynamics. The deep learning-based methods suffer from the spatial, temporal and spectral uncertainties in STF and the mode collapse problem of generative adversarial networks (GANs) for remote sensing images with complex scenes. To address these problems, we propose a novel spatiotemporal fusion method with diffusion models (STFDiff) that merges a coarse image at the prediction date and the coarse-fine image pairs acquired at other dates to generate the fine image at the prediction date. STFDiff generates the fine image via repeated refinement with initialized Gaussian noise under the control of the prior images acquired at other dates. At each iteration, the noise is predicted through a conditional noise predictor dual-stream Unet (DS-Unet), which enhances the noise features by subtracting the extracted features from the dual-stream encoders (DS-encoders). The noise is then gradually removed, and finally the fine image is generated with similar spatial details to the fine images and temporal dynamics to the coarse images. Comprehensive experiments on two public datasets and one personally collected dataset demonstrate that STFDiff outperforms state-of-the-art (SOTA) methods. To further verify the applicability of STFDiff on downstream tasks, we compared the K-means clustering results on the fusion images generated by different methods. The results show that the classification results of STFDiff are the most consistent with the actual images and obtain ∼2% mean intersection over union (mIoU) improvement over the SOTA methods. The source code is available at https://github.com/prowDIY/STF.
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
u深度完成签到 ,获得积分10
2秒前
万能图书馆应助超级灵竹采纳,获得10
2秒前
阳光的紊完成签到,获得积分10
2秒前
xyx发布了新的文献求助10
2秒前
曦之南。完成签到,获得积分10
2秒前
silence63完成签到 ,获得积分10
2秒前
翎儿响叮当完成签到 ,获得积分10
3秒前
丝垚完成签到 ,获得积分10
4秒前
丘比特应助贺知什么书采纳,获得10
4秒前
CipherSage应助112我的采纳,获得10
6秒前
隐形曼青应助土豪的觅翠采纳,获得10
6秒前
8秒前
ding应助3333采纳,获得10
8秒前
dududuudu完成签到 ,获得积分10
9秒前
李健应助HY采纳,获得10
10秒前
yang完成签到,获得积分10
12秒前
13秒前
xyx完成签到,获得积分10
14秒前
weiyf15完成签到 ,获得积分10
14秒前
353851547crf完成签到,获得积分10
15秒前
16秒前
c2发布了新的文献求助20
17秒前
Aman发布了新的文献求助10
17秒前
19秒前
19秒前
20秒前
快乐的小叮当应助橙子采纳,获得10
20秒前
20秒前
bbh发布了新的文献求助10
20秒前
小阳发布了新的文献求助10
22秒前
今后应助科研爱好者采纳,获得10
22秒前
23秒前
23秒前
23秒前
HY发布了新的文献求助10
24秒前
陈曦发布了新的文献求助10
25秒前
打打应助如此这般采纳,获得10
25秒前
JamesPei应助morena采纳,获得10
25秒前
112我的发布了新的文献求助10
27秒前
3333发布了新的文献求助10
28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 3989510
求助须知:如何正确求助?哪些是违规求助? 3531756
关于积分的说明 11254536
捐赠科研通 3270255
什么是DOI,文献DOI怎么找? 1804947
邀请新用户注册赠送积分活动 882113
科研通“疑难数据库(出版商)”最低求助积分说明 809176