The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Pixel-wise regression using U-Net and its application on pansharpening
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Models like U-Net [58] and DeepLab [59] can be used for pixel-wise classification, which is useful in highly dense and occluded environments when individuals need to be segmented precisely and distinguish people from backgrounds at a pixel level.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [58]
Citations
Cites
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
Cited by
Sorted by citations
Sun, A. Y., & Scanlon, B. R. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab1b7d · Cited by 585
Vivone, G., Mura, M. D., Garzelli, A., Restaino, R., Scarpa, G., Ulfarsson, M. O., Alparone, L., & Chanussot, J. (2020) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.3019315 · Cited by 364
Rundo, L., Han, C., Nagano, Y., Zhang, J., Hataya, R., Militello, C., Tangherloni, A., Nobile, M. S., Ferretti, C., Besozzi, D., et al. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.07.006 · Cited by 290
Machine Learning in Pansharpening: A benchmark, from shallow to deep networks
Deng, L.-J., Vivone, G., Paoletti, M. E., Scarpa, G., He, J., Zhang, Y., Chanussot, J., & Plaza, A. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2022.3187652 · Cited by 260
Hyperspectral Image Super-Resolution via Deep Spatiospectral Attention Convolutional Neural Networks
Hu, J., Huang, T., Deng, L.-J., Jiang, T.-X., Vivone, G., & Chanussot, J. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084682 · Cited by 241
Fringe projection profilometry by conducting deep learning from its digital twin
Zheng, Y., Wang, S., Li, Q., & Li, B. (2020) · Optics Express · doi:10.1364/oe.410428 · Cited by 147
Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement
Tsagkatakis, G., Aidini, A., Fotiadou, K., Giannopoulos, M., Pentari, A., & Tsakalides, P. (2019) · Sensors · doi:10.3390/s19183929 · Cited by 144
Zhang, K., Zhang, F., Wan, W., Yu, H. L., Sun, J., Ser, J. D., Elyan, E., & Hussain, A. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.12.026 · Cited by 138
Gadosey, P. K., Li, Y., Agyekum, E. A., Zhang, T., Liu, Z., Yamak, P. T., & Essaf, F. (2020) · Diagnostics · doi:10.3390/diagnostics10020110 · Cited by 85
Satellite derived bathymetry using deep learning
Najar, M. A., Thoumyre, G., Bergsma, E. W. J., Almar, R., Benshila, R., & Wilson, D. G. (2021) · Machine Learning · doi:10.1007/s10994-021-05977-w · Cited by 84
Doury, A., Somot, S., Gadat, S., Ribes, A., & Corre, L. (2022) · Climate Dynamics · doi:10.1007/s00382-022-06343-9 · Cited by 113
Multi-scale fully convolutional network for gland segmentation using three-class classification
Ding, H., Pan, Z., Cen, Q., Li, Y., & Chen, S. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.097 · Cited by 76
Vu, M. T., & Jardani, A. (2021) · Geophysical Journal International · doi:10.1093/gji/ggab024 · Cited by 56
Multi-Focus Image Fusion Using U-Shaped Networks With a Hybrid Objective
Li, H., Nie, R., Cao, J., Guo, X., Zhou, D., & He, K. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2928818 · Cited by 52
Radhakrishnan, K., Scott, K. A., & Clausi, D. A. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3076109 · Cited by 45
Multiresolution Analysis Based on Dual-Scale Regression for Pansharpening
Wang, P., Yao, H., Cai, L., Zhang, G., & Leung, H. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3131477 · Cited by 49
Wang, W., McMechan, G. A., Ma, J., & Xie, F. (2020) · Geophysics · doi:10.1190/geo2020-0423.1 · Cited by 47
Deep residual dense U-Net for resolution enhancement in accelerated MRI acquisition
Ding, P. L. K., Li, Z., Zhou, Y., & Li, B. (2019) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2513158 · Cited by 42
Seo, S., Choi, J.-S., Lee, J., Kim, H.-H., Seo, D., Jeong, J., & Kim, M. (2020) · IEEE Access · doi:10.1109/access.2020.3035802 · Cited by 42
Dong, G., Wong, J. C., Lestandi, L., Mikula, J., Vastola, G., Jhon, M. H., Dao, M. H., Kizhakkinan, U., Ford, C. S., & Rosen, D. W. (2022) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2022.117541 · Cited by 41
Objective assessment of segmentation models for thyroid ultrasound images
Yadav, N., Dass, R., & Virmani, J. (2022) · Journal of Ultrasound · doi:10.1007/s40477-022-00726-8 · Cited by 41
A Comparison of Machine Learning Approaches to Improve Free Topography Data for Flood Modelling
Meadows, M., & Wilson, M. D. (2021) · Remote Sensing · doi:10.3390/rs13020275 · Cited by 40
Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach
Chen, Y., Wei, Y., Wang, Q., Chen, F., Lu, C., & Lei, S. (2020) · Remote Sensing · doi:10.3390/rs12172767 · Cited by 38
Phase Extraction from Single Interferogram Including Closed-Fringe Using Deep Learning
Kando, D., Tomioka, S., Miyamoto, N., & Ueda, R. (2019) · Applied Sciences · doi:10.3390/app9173529 · Cited by 35
Deep-Learning-Driven Full-Waveform Inversion for Ultrasound Breast Imaging
Robins, T., Camacho, J., Agudo, Ò. C., Herraiz, J. L., & Guasch, L. (2021) · Sensors · doi:10.3390/s21134570 · Cited by 34
Image Fusion in Remote Sensing: An Overview and Meta-Analysis
Albanwan, H., Qin, R., & Tang, Y. (2024) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.24-00110r1 · Cited by 33
Perry, B. J., Guo, Y., & Mahmoud, H. N. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104022 · Cited by 33
Structural Similarity Loss for Learning to Fuse Multi-Focus Images
Yan, X., Gilani, S. Z., Qin, H., & Mian, A. S. (2020) · Sensors · doi:10.3390/s20226647 · Cited by 30
Ready-to-use deep-learning surrogate models for problems with spatially variable inputs and outputs
He, X., Xu, H., & Sheng, D. (2022) · Acta Geotechnica · doi:10.1007/s11440-022-01706-2 · Cited by 31
Using three dimensional convolutional neural networks for denoising echosounder point cloud data
Stephens, D., Smith, A., Redfern, T., Talbot, A., Lessnoff, A., & Dempsey, K. (2019) · Applied Computing and Geosciences · doi:10.1016/j.acags.2019.100016 · Cited by 26
Automatic detection and segmentation of optic disc using a modified convolution network
Maiti, S., Maji, D., Dhara, A. K., & Sarkar, G. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103633 · Cited by 24
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
Nguyen, B., Tian, G., Во, М. Т. Л., Michel, A., Corpetti, T., & Granero-Belinchón, C. (2022) · European Signal Processing Conference · doi:10.23919/eusipco55093.2022.9909569 · Cited by 25
Band-Independent Encoder–Decoder Network for Pan-Sharpening of Remote Sensing Images
Liu, C., Zhang, Y., Wang, S., Sun, M., Ou, Y., Wan, Y., & Liu, X. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2975230 · Cited by 24
Liu, J., Zhang, H., Tian, J.-H., Su, Y., Chen, Y., & Wang, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3412033 · Cited by 23
Lilienkamp, H., von Specht, S., Weatherill, G. A., Caire, G., & Cotton, F. (2022) · Bulletin of the Seismological Society of America · doi:10.1785/0120220008 · Cited by 26
Deep Learning-Enabled Technologies for Bioimage Analysis
Rabbi, F., Dabbagh, S. R., Angın, P., Yetisen, A. K., & Taşoğlu, S. (2022) · Micromachines · doi:10.3390/mi13020260 · Cited by 23
Fully Automatic Intervertebral Disc Segmentation Using Multimodal 3D U-Net
Wang, C., Guo, Y., Chen, W., & Yu, Z. (2019) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2019.00109 · Cited by 22
Ma, L., Rathgeb, A. P., Mubarak, H. K., Tran, M. H., & Fei, B. (2022) · Journal of Biomedical Optics · doi:10.1117/1.jbo.27.5.056502 · Cited by 19
Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24
A self-adaptive wildfire detection algorithm by fusing physical and deep learning schemes
Jin, S., Wang, T., Huang, H., Zheng, X., Li, T., & Guo, Z. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103671 · Cited by 18
KUnet: Microscopy Image Segmentation With Deep Unet Based Convolutional Networks
Chang, S., & Liao, S. W. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914048 · Cited by 16
Analysis of pansharpening methods for multispectral satellite imagery
Fazilov, S. K., Yusupov, O. R., Radjabov, S., Eshonqulov, E., & Abdieva, K. (2024) · AIP conference proceedings · doi:10.1063/5.0241414 · Cited by 15
Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design
Wong, J. C., Ooi, C. C., Chattoraj, J., Lestandi, L., Dong, G., Kizhakkinan, U., Rosen, D. W., Jhon, M. H., & Dao, M. H. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022022 · Cited by 15
Automated Extraction of a Depth-Defined Wave Runup Time Series From Lidar Data Using Deep Learning
Collins, A. M., O’Dea, A., Brodie, K., Bak, A. S., Hesser, T., Spore, N., & Farthing, M. W. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3244488 · Cited by 15
Literature review on deep learning for the segmentation of seismic images
Monteiro, B. A. A., Canguçu, G. L., Jorge, L. M. S., Vareto, R. H., Oliveira, B. S., Silva, T. H., Lima, L. A., Machado, A. M. C., Schwartz, W. R., & de Melo, P. O. S. V. (2024) · Earth-Science Reviews · doi:10.1016/j.earscirev.2024.104955 · Cited by 17
Recent Studies on Void Shrinkage in Metallic Materials Subjected to In Situ Heavy Ion Irradiations
Niu, T., Nasim, M., Annadanam, R. G. S., Fan, C., Li, J., Shang, Z., Xue, Y., El-Azab, A., Wang, H., & Zhang, X.-H. (2020) · JOM · doi:10.1007/s11837-020-04358-3 · Cited by 18
An Improved Version of the Generalized Laplacian Pyramid Algorithm for Pansharpening
Addesso, P., Restaino, R., & Vivone, G. (2021) · Remote Sensing · doi:10.3390/rs13173386 · Cited by 14
Detecting spiral wave tips using deep learning
Lilienkamp, H., & Lilienkamp, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-99069-3 · Cited by 13
Texture feed based convolutional neural network for pansharpening
Imani, M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.083 · Cited by 13
Li, Z., Tao, W., Liu, J., Zhu, F., Du, G., & Ji, G. (2023) · Horticulturae · doi:10.3390/horticulturae9091034 · Cited by 12
Pansharpening and spatiotemporal image fusion method for remote sensing
Anand, S., & Sharma, R. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad3a34 · Cited by 14
Data-driven surrogate modelling of residual stresses in Laser Powder-Bed Fusion
Lestandi, L., Wong, J. C., Ge, D., Kuehsamy, S. J., Mikula, J., Vastola, G., Kizhakkinan, U., Ford, C. S., Rosen, D. W., Dao, M. H., et al. (2023) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2023.2257628 · Cited by 11
Malinowska, D., Milillo, P., Briggs, K., Reale, C., & Giardina, G. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3449688 · Cited by 15
Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images
Yadav, N., Dass, R., & Virmani, J. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02849-4 · Cited by 11
Lesion probability mapping in MS patients using a regression network on MR fingerprinting
Hermann, I., Golla, A., Martínez‐Heras, E., Schmidt, R., Solana, E., Llufriú, S., Gass, A., Schad, L. R., & Zöllner, F. G. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00636-x · Cited by 11
Delgado-Enales, I., Lizundia-Loiola, J., Molina-Costa, P., & Ser, J. D. (2025) · Urban Climate · doi:10.1016/j.uclim.2025.102415 · Cited by 10
Lee, H. C., Park, Y. K., Duan, X., Jia, X., Jiang, S. B., & Yang, M. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abab57 · Cited by 10
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation
Pendse, M., Thangarasa, V., Chiley, V., Holmdahl, R., Hestness, J., & DeCoste, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72087-2_34 · Cited by 10
Correction of Low Vegetation Impact on UAV-Derived Point Cloud Heights With U-Net Networks
Gruszczyński, W., Puniach, E., Ćwiąkała, P., & Matwij, W. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3057272 · Cited by 10
GEORGIA: A Graph Neural Network Based EmulatOR for Glacial Isostatic Adjustment
Lin, Y.-C., Whitehouse, P., Valentine, A. P., & Woodroffe, S. (2023) · Geophysical Research Letters · doi:10.1029/2023gl103672 · Cited by 13
A collaborative representation-based approximation method for remote sensing image fusion
Imani, M. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2019.1654143 · Cited by 9
W-NetPan: Double-U network for inter-sensor self-supervised pan-sharpening
Fernández-Beltrán, R., Fernandez, R., Kang, J. S., & Pla, F. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.002 · Cited by 7
Qiao, B., Xu, B., Xie, Y., Lin, Y., Liu, Y., & Zuo, X. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/4725986 · Cited by 8
Balanced spatio-spectral feature extraction for hyperspectral and multispectral image fusion
Rajaei, A., Abiri, E., & Helfroush, M. S. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109391 · Cited by 8
Identification of Anomaly Scenes in Videos Using Graph Neural Networks
Masood, K., Al-Sakhnini, M. M., Nawaz, W., Faiz, T., Mohammad, A. S., & Kashif, H. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.033590 · Cited by 7
Deep-learning approaches for pixel-level pansharpening
Yang, Y., Su, Z. Y., HUANG, S., Wei-guo, W., Tu, W., & Lu, H. (2022) · National Remote Sensing Bulletin · doi:10.11834/jrs.20211325 · Cited by 8
Tao, H., Yuan, G., Li, H., & Li, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132527 · Cited by 6
Applications, image analysis, and interpretation of computer vision in medical imaging
Matsuzaka, Y., & Iyoda, M. (2026) · Frontiers in Radiology · doi:10.3389/fradi.2025.1733003 · Cited by 12
Huang, L., An, R., Zhao, S., Jiang, T., & Hu, H. (2020) · Remote Sensing · doi:10.3390/rs12091441 · Cited by 6
Bialek, S., Bertin, E., Fabbro, S., Bouy, H., Rivet, J., Lai, O., & Cuillandre, J. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1018 · Cited by 6
Pan-Sharpening With Color-Aware Perceptual Loss And Guided Re-Colorization
Bello, J. L. G., Seo, S., & Kim, M. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190785 · Cited by 6
He, J., Yuan, Q., Li, J., & Zhang, L. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3186916 · Cited by 6
Chen, J., Cao, C., Shen, S., Guo, T., & Liang, J. (2025) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2025.111887 · Cited by 5
Nair, A. V., Lin, C., Hsu, F., Wong, T.-H., Chuang, S., Lin, Y.-S., Chen, C., Campagnola, P. J., Lien, C.-H., & Chen, S.-J. (2023) · Scientific Reports · doi:10.1038/s41598-023-46417-0 · Cited by 5
Sihvonen, T., Duma, Z.-S., & Рейникайнен, С.-П. (2023) · Micron · doi:10.1016/j.micron.2023.103578 · Cited by 5
Unsupervised super resolution network for hyperspectral histologic imaging
Ma, L., Rathgeb, A. P., Tran, M. H., & Fei, B. (2022) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2611889 · Cited by 5
Müller, K., Leppich, R., Geiß, C., Borst, V., Pelizari, P. A., Kounev, S., & Taubenböck, H. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3297710 · Cited by 5
Kherazi, F. Z., Sun, D., Sohu, J. M., Rhadiouini, C. E., & Shaikh, S. N. (2026) · Water Resources Management · doi:10.1007/s11269-025-04354-9 · Cited by 4
Pansharpening via Super-Resolution Iterative Residual Network With a Cross-Scale Learning Strategy
Chen, S., Qi, H., & Nan, K. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3138096 · Cited by 4
O-Net: Dangerous Goods Detection in Aviation Security Based on U-Net
Kim, W., Jun, S., Kang, S., & Lee, C. (2020) · IEEE Access · doi:10.1109/access.2020.3037719 · Cited by 5
Spatial Dynamic Selection Network for Remote-Sensing Image Fusion
Hu, J., Hu, P., Wang, Z., Kang, X., Fan, S., & Dun, M. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3085140 · Cited by 4
Convolutional Neural Networks and Recurrent Neural Networks
Yan, W. Q. (2023) · Texts in computer science · doi:10.1007/978-981-99-4823-9_3 · Cited by 8
Five deep learning recipes for the mask-making industry
Baranwal, A. K., Meyer, M., Nguyen, T., Pillai, S., Nakayamada, N., Wahlsten, M. L., Fujimura, A., Niewczas, M., & Pomerantsev, M. (2019) · SPIE Photomask Technology Conference · doi:10.1117/12.2538440 · Cited by 4
Pix2Streams: Dynamic Hydrology Maps from Satellite-LiDAR FusionPreprint
Garcia, D., Mateo‐García, G., Bernhardt, H., Hagensieker, R., López-Francos, I. G., Stock, J. D., Schumann, G. J.-P., Dobbs, K., & Kalaitzis, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07584 · Cited by 6
Rao, P. K., & Chatterjee, S. (2021) · Research Square · doi:10.21203/rs.3.rs-140504/v1 · Cited by 4
Maskpan: Mask Prior Guided Network For Pansharpening
Rui, X., Cao, Y. H., Kang, Y., Song, W., & Ba, R. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190853 · Cited by 3
Shelton, J. A., Polewski, P., & Yao, W. (2020) · International Workshop Climate Informatics · doi:10.1145/3429309.3429328 · Cited by 3
Self-citationResidual Network with Attention to Neural Cells Segmentation
Abbas, R. F., & Abdulmunim, M. E. (2023) · Iraqi Journal of Science · doi:10.24996/ijs.2023.64.4.37 · Cited by 3
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural NetworksPreprint
Hu, J., Huang, T., Deng, L.-J., Jiang, T.-X., Vivone, G., & Chanussot, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.14400 · Cited by 3
Siao, H.-Y., Qi, S., Ding, Z. J., Lin, C., Hsieh, Y.-C., & Chen, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.13495 · Cited by 3
SAR Image Formation: Conventional and AI-Based Approaches on Sentinel-1 Raw Products
Campagna, G. M., & Manca, L. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-25755-1_1 · Cited by 2
A cost-effective method for mapping land cover at national scale
Dymond, J. R., Shepherd, J. D., Law, R., Martin, B., Schindler, J., & Belliss, S. E. (2026) · Science of Remote Sensing · doi:10.1016/j.srs.2026.100376 · Cited by 2
U-Net based Mapping from Digital Images to Laser Doppler Imaging for Burn Assessment
Rozo, A., Miskovic, V., Rose, T., Keersebilck, E., Iorio, C. S., & Varon, C. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871759 · Cited by 2
Segmentation of CT-Scan Images Using UNet Network for Patients Diagnosed with COVID-19
Gois, F. N. B., & Marques, J. A. L. (2023) · Springer eBooks · doi:10.1007/978-3-031-30788-1_3 · Cited by 2
Bernstein, B., Li, Y., & Hammack, R. (2023) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/image2023-3912400.1 · Cited by 2
Postprocessing of convection permitting precipitation forecast using UNets
Esquivel-González, M., González, A., Pérez, J. C., & Díaz, J. P. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103255 · Cited by 2
Bed Topography Inference from Velocity Field Using Deep Learning
Oshtorjani, M. K., & Ancey, C. (2023) · Water · doi:10.3390/w15234055 · Cited by 2
Transfer Learning and Ensemble Learning
Yan, W. Q. (2023) · Texts in computer science · doi:10.1007/978-981-99-4823-9_7 · Cited by 1
McGee, K. P., Sui, Y., Witte, R. J., Panda, A., Campeau, N. G., Mostardeiro, T. R., Sobh, N., Ravaioli, U., Zhang, S., Falahkheirkhah, K., et al. (2024) · Frontiers in Radiology · doi:10.3389/fradi.2024.1498411 · Cited by 1
End-to-end deep learning for tuning-free non-contrast ultrasound microvessel imaging
Minhaz, A. T., Cooley, M. B., Subramaniam, A., Exner, A. A., Orge, F. H., Wilson, D. L., & Bayat, M. (2022) · IEEE International Ultrasonics Symposium · doi:10.1109/ius54386.2022.9958473 · Cited by 1
Metadata
- Authors
- Yao, W., Zeng, Z., Lian, C., & Tang, H.
- Affiliations
- South Central Minzu University (CN)
- Huazhong University of Science and Technology (CN)
- Wuhan University of Technology (CN)
- China University of Geosciences (CN)
- Corresponding author
- Zeng, Zhigang
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 312
- Pages
- 364-371
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2807567802
- Cited by
- 133
- References
- 38
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 12 · 2023: 20 · 2022: 20 · 2021: 26 · 2020: 21 · 2019: 16
- FWCI
- 11.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- Natural Science Foundation of China (61673188, 61503144, 61761130081)
- Natural Science Foundation of Hubei Province (2017CFB519)
- Topics
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- pansharpening, U-Net, convolutional neural networks, remote sensing images, multi-resolution features, geometric deformation, color distortion, image fusion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review