Bishift Networks for Thick Cloud Removal with Multitemporal Remote Sensing Images

Long, Chaojun; Li, Xinghua; Jing, Yinghong; Shen, Huanfeng

doi:10.1155/2023/9953198

Cited by 21 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(5), the captured image Ic(x) is known information, whereas the ambient light parameter Bc and the direct transmission parameter eβcDd are unknown information. Since deep learning networks have been shown to work well in many applications, 14 , 15 we follow this idea. The dark channel prior algorithm 16 proved that ambient light Bc could be extracted from a captured image as global information, so a background scattering information estimation network can be constructed to estimate Bc.…”

Section: Proposed Methodsmentioning

confidence: 99%

Deep network for underwater image enhancement inspired by physical imaging model

Tang,

Song,

Liu

2023

Opt. Eng.

View full text Add to dashboard Cite

Section: Proposed Methodsmentioning

confidence: 99%

Deep network for underwater image enhancement inspired by physical imaging model

Tang,

Song,

Liu

2023

Opt. Eng.

View full text Add to dashboard Cite

“…They introduce RD-UNet, a deep learning model, showing superiority over current methods. Another research effort [212] focuses on cloud removal in remote sensing images using deep learning methods. Traditional methods like exemplarbased and information cloning show inconsistencies in feature reconstruction.…”

Section: B Real-time Data Processingmentioning

confidence: 99%

Remote Sensing for Agriculture in the Era of Industry 5.0—A Survey

Victor,

Maddikunta,

Mary

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Agriculture can be regarded as the backbone of human civilization. As technology evolved, the synergy between agriculture and remote sensing has brought about a paradigm shift, thereby entirely revolutionizing the traditional agricultural practices. Nevertheless, the adoption of remote sensing technologies in agriculture face various challenges in terms of limited spatial and temporal coverage, high cloud cover, low data quality and so on. Industry 5.0 marks a new era in the industrial revolution, where humans and machines collaborate closely, leveraging their distinct capabilities, thereby enhancing the decision making capabilities, sustainability and resilience. This paper provides a comprehensive survey on remote sensing technologies and related aspects in dealing with the various agricultural practices in the Industry 5.0 (I5.0) era. We also elaborately discuss the various applications pertaining to I5.0enabled remote sensing for agriculture. Finally, we discuss several challenges and issues related to the integration of I5.0 technologies in agricultural remote sensing. This comprehensive survey on remote sensing for agriculture in Industry 5.0 era offers valuable insights into the current state, challenges, and potential advancements in the integration of remote sensing technologies and Industry 5.0 principles in agriculture, thus paving the way for future research, development, and implementation strategies in this domain.

show abstract

“…Based on the research experience of scholars, we selected U-Net [9], DeepLab V3 + [10], RFR-Net [45], and STS-CNN [46] as comparison algorithms, and all models were retrained on our dataset and analyzed for quantitative and visual comparisons [47]. Among them, U-Net and DeepLabV3+ are the classical networks in the field of semantic segmentation and the first classical networks to reconstruct images.…”

Section: ) Comparison Algorithm and Metricsmentioning

confidence: 99%

DecRecNet: A Decoupling-Reconstruction Network for Restoring the Missing Information of Optical Remote Sensing Images

Liu,

Cui,

Jiang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

Temporal-based methods effectively improve the utilization rate of remote sensing images, but large ratios of missing information still need to be improved in the reconstruction models. In this paper, based on the imaging theory with the help of a radiation correction model, a decoupling-reconstruction network (DecRecNet) for image reconstruction is proposed. The network uses a ground content radiation (GCR) correction module and imaging environment radiation (IER) correction module and their corresponding loss functions to decouple the image radiation information into ground content radiation related to the ground objects and imaging environment radiation related to the imaging conditions and carry out targeted processing to achieve the purpose of missing information reconstruction. The ground content radiation consistency loss function is used to preserve the ground content information, and the imaging environment radiation consistency loss function and imaging environment radiation smoothness loss are used to coordinate the imaging environment. The radiation guiding module further performs targeted radiation adjustment on the foreground and background images to transfer the background imaging environment to the foreground and consistent radiation information of the same ground object. Compared with the classical U-Net, DeepLabV3+, RFR-Net, and STS-CNN methods, our model showed remarkable advantages in cloud occlusion and stripes of Landsat-8 (30 m), GaoFen-1 (2 m), and Landsat-7 (30 m) at various missing ratios, data sources, resolutions, and scenes, and achieving the goal of missing information reconstruction in large missing ratios.

show abstract

Bishift Networks for Thick Cloud Removal with Multitemporal Remote Sensing Images

Cited by 21 publications

References 43 publications

Deep network for underwater image enhancement inspired by physical imaging model

Deep network for underwater image enhancement inspired by physical imaging model

Remote Sensing for Agriculture in the Era of Industry 5.0—A Survey

DecRecNet: A Decoupling-Reconstruction Network for Restoring the Missing Information of Optical Remote Sensing Images

Contact Info

Product

Resources

About