Pixel-wise regression using U-Net and its application on pansharpening

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Image quality assessment: from error visibility to structural similarity

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Multilayer feedforward networks are universal approximators

Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728

Image Super-Resolution Using Deep Convolutional Networks

Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105

Multilayer feedforward networks are universal approximators

HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,266

Receptive fields and functional architecture of monkey striate cortex

Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,685

Machine Learning in the Identification and Classification of Tree Species

Дмитриевич, Ж. К., Свойкин, Ф. В., Svoikin, V. F., Mishura, L. G., Kabakov, V. V., & Shkarubo, S. N. (2025) · Russian Engineering Research · doi:10.3103/s1068798x25700741 · Cited by 1

An Improved Injection Model for Pansharpening Based on Weighted Least Squares

Shi, Y. C., Wang, W., & Tan, A. (2021) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei53629.2021.9624441 · Cited by 1

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep RegressionPreprint

Siracusano, G., & Corte, A. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.10865 · Cited by 1

Computer-Assisted Analysis of Biomedical ImagesPreprint

Rundo, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04381 · Cited by 3

Guiding 3D U-nets with signed distance fields for creating 3D models from imagesPreprint

Juhl, K. A., Paulsen, R. R., Dahl, A. B., Dahl, V. A., Backer, O. D., Kofoed, K. F., & Cámara, Ó. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.10579 · Cited by 1

HiCoRe: Visual Hierarchical Context-ReasoningPreprint

Bugatti, P. H., Saito, P. T. M., & Davis, L. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00848 · Cited by 1

A pilot exploratory study of the potentials of deep learning methods in cancer image segmentation and classificationPreprint

Yi, E., & Liu, Y. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/820563 · Cited by 1

A general approach to bridge the reality-gapPreprint

Lomnitz, M., Hampel-Arias, Z., Lopatina, N., & Mejia, F. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.01865 · Cited by 1

Pan-Sharpening with Color-Aware Perceptual Loss and Guided Re-ColorizationPreprint

Bello, J. L. G., Seo, S., & Kim, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.16583

CapsNet and Manifold Learning

Yan, W. Q. (2020) · Texts in computer science · doi:10.1007/978-3-030-61081-4_6

Convolutional neural network with U-Net architecture to detect buildings in satellite imagery for statistical purposes

Kharis, M., & Mariyah, S. (2021) · Statistical Journal of the IAOS · doi:10.3233/sji-200781

In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite ImageryPreprint

Shelton, J. A., Polewski, P., & Yao, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.02406

Pansformers: Transformer-Based Self-Attention Network for PansharpeningPreprint

R, N. G., Nitish Kumar, M., Venkateswaran, N., Kakani, R., Gupta, U., & Garg, A. (2021) · doi:10.36227/techrxiv.17153228

Pansformers: Transformer-Based Self-Attention Network for PansharpeningPreprint

R, N. G., Nitish Kumar, M., Venkateswaran, N., Kakani, R., Gupta, U., & Garg, A. (2021) · doi:10.36227/techrxiv.17153228.v1

Bibliography

(2021) · Wiley eBooks · doi:10.1002/9781119646181.biblio

U-Net based Semantic Segmentation for Touchless Fingerprint Technology: A Reflective Review

Nahar, P., Gupta, P., & Kaur, H. (2022) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi56455.2022.10119359

Introduction

Yan, W. Q. (2023) · Texts in computer science · doi:10.1007/978-981-99-4823-9_1

Manifold Learning and Graph Neural Network

Yan, W. Q. (2023) · Texts in computer science · doi:10.1007/978-981-99-4823-9_6

Intelligent and Robust 3D shape measurement for in-situ applications

Zheng, Y. (2022) · doi:10.31274/td-20240329-437

An Exploration of Voxel/Connection and Image Segmentation Approaches to Derive High Resolution Rasters of Manning’s n

McGrath, H., Turgeon-Pelchat, M., Houngbo, H., Zhou, L., & Langari, Z. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10640595

MAPunet: High-resolution snow depth mapping through U-Net pixel-wise regression

Betato, A., Díaz, H., French, N., James, T., & Remeseiro, B. (2025) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2025.101477 · Cited by 2

Reconstruction of Heart-related Imaging from Lung Electrical Impedance Tomography Using Semi-Siamese U-Net

Ko, Y.-F., Lin, Y., & Su, P. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056408077250610070821

VIE-Net: Regressive U-Net for Vegetation Index Estimation

Capparella, V., Nemmi, E. N., Violino, S., Costa, C., Figorilli, S., Moscovini, L., Pallottino, F., Pane, C., Mei, A., & Ortenzi, L. (2025) · IEEE Access · doi:10.1109/access.2025.3598124 · Cited by 1

Improving New Zealand’s Vegetation Mapping Using Weakly Supervised Learning

Martin, B., Mason, N. W. H., Shepherd, J. D., & Schindler, J. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3635413

Predicting parameters of a model cuprate superconductor using machine learning

Ulitko, V. A., Yasinskaya, D. N., Bezzubin, S. A., Koshelev, A. A., & Panov, Y. D. (2026) · Computational Materials Science · doi:10.1016/j.commatsci.2026.114621

Additional file 1 of Lesion probability mapping in MS patients using a regression network on MR fingerprinting

Hermann, I., Golla, A. K., Martínez‐Heras, E., Schmidt, R., Solana, E., Llufriú, S., Gass, A., Schad, L. R., & Zöllner, F. G. (2021) · Figshare · doi:10.6084/m9.figshare.14936167

Assessing Knowledge Distillation of a Multi-Emitter Localizing Neural Network for Applications in Stochastic Optical Reconstruction MicroscopyPreprint

Reed, M., & Zadegan, R. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.19.694650

Avancées méthodologiques dans l’estimation de la température de surface continentale par des observations microondes passives

Favrichon, S. (2021) · theses.fr (ABES) · doi:10.70675/a96ef528za8b3z4ca1zaac5z98817ca427bc

Robust estimation of regional climate change : construction of an hybrid approach between deep neural networks and climate models

Doury, A. (2022) · theses.fr (ABES) · doi:10.70675/6b43e31az1783z459fzaefcz951beef65ed1

An Application of Image Processing Techniques in the Calibration of Catastrophe Models

Rotich, T. K., Hambardzumyan, A., Koech, E., Poghosyan, O., & Mung'atu, J. K. (2025) · Variance · doi:10.66573/001c.146426

Pseudo‐Radar Reflectivity by Applying U‐Net Models to Global Passive Microwave Brightness Temperatures

Morvais, F., & Liu, C. (2026) · Journal of Geophysical Research Atmospheres · doi:10.1029/2026jd046570

The application and progress of AI-based image analysis in tumor organoid research

Xu, Y., Fan, S., Chen, Y., Xu, W., Lu, H., Zhou, Y., Liu, Y., Du, Q., Wang, W., Yu, T., et al. (2026) · iScience · doi:10.1016/j.isci.2026.116756

Deep Learning—Based Spatial Downscaling of Land Surface Temperature: A Comparative Study of U-Net, ResNet, and Hybrid Models

Nontarak, N., Simachokchai, K., & Kitratporn, N. (2026) · International Conference on Geoinformatics · doi:10.1109/geoinformatics72247.2026.11665445

Pansharpening by Convolutional Neural Networks

Masi, G., Cozzolino, D., Verdoliva, L., & Scarpa, G. (2016) · Remote Sensing · doi:10.3390/rs8070594 · Cited by 1,191

CNN-based pansharpening of multi-resolution remote-sensing images

Masi, G., Cozzolino, D., Verdoliva, L., & Scarpa, G. (2017) · Joint Urban Remote Sensing Event (JURSE) · doi:10.1109/jurse.2017.7924534 · Cited by 35

Transformer-Based Regression Network for Pansharpening Remote Sensing Images

Su, X., Li, J., & Hua, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3152425 · Cited by 110

A residual convolutional neural network for pan-shaprening

Rao, Y., He, L., & Zhu, J. (2017) · International Workshop on Remote Sensing with Intelligent Processing (RSIP) · doi:10.1109/rsip.2017.7958807 · Cited by 74

Pansharpening scheme using spatial detail injection–based convolutional neural networks

Saxena, N., Saxena, G., Khare, N., & Rahman, M. H. (2022) · IET Image Processing · doi:10.1049/ipr2.12384 · Cited by 5