Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images DOI Creative Commons

Zilong Lian,

Yulin Zhan, Wenhao Zhang

и другие.

Sensors, Год журнала: 2025, Номер 25(4), С. 1093 - 1093

Опубликована: Фев. 12, 2025

Remote sensing images captured by satellites play a critical role in Earth observation (EO). With the advancement of satellite technology, number and variety remote have increased, which provide abundant data for precise environmental monitoring effective resource management. However, existing imagery often faces trade-off between spatial temporal resolutions. It is challenging single to simultaneously capture with high Consequently, spatiotemporal fusion techniques, integrate from different sensors, garnered significant attention. Over past decade, research on has achieved remarkable progress. Nevertheless, traditional methods encounter difficulties when dealing complicated scenarios. development computer science, deep learning models, such as convolutional neural networks (CNNs), generative adversarial (GANs), Transformers, diffusion recently been introduced into field fusion, resulting efficient accurate algorithms. These algorithms exhibit various strengths limitations, require further analysis comparison. Therefore, this paper reviews literature learning-based methods, analyzes compares algorithms, summarizes current challenges field, proposes possible directions future studies.

Язык: Английский

Evaluating the consistency between Sentinel-2 and Planet constellations at field scale: illustration over winter wheat DOI

Yuman Ma,

Wenjuan Li,

Jingwen Wang

и другие.

Precision Agriculture, Год журнала: 2025, Номер 26(2)

Опубликована: Фев. 12, 2025

Язык: Английский

Процитировано

0
Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images DOI Creative Commons

Zilong Lian,

Yulin Zhan, Wenhao Zhang

и другие.

Sensors, Год журнала: 2025, Номер 25(4), С. 1093 - 1093

Опубликована: Фев. 12, 2025

Remote sensing images captured by satellites play a critical role in Earth observation (EO). With the advancement of satellite technology, number and variety remote have increased, which provide abundant data for precise environmental monitoring effective resource management. However, existing imagery often faces trade-off between spatial temporal resolutions. It is challenging single to simultaneously capture with high Consequently, spatiotemporal fusion techniques, integrate from different sensors, garnered significant attention. Over past decade, research on has achieved remarkable progress. Nevertheless, traditional methods encounter difficulties when dealing complicated scenarios. development computer science, deep learning models, such as convolutional neural networks (CNNs), generative adversarial (GANs), Transformers, diffusion recently been introduced into field fusion, resulting efficient accurate algorithms. These algorithms exhibit various strengths limitations, require further analysis comparison. Therefore, this paper reviews literature learning-based methods, analyzes compares algorithms, summarizes current challenges field, proposes possible directions future studies.

Язык: Английский

Процитировано

0