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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Multilayer feedforward networks are universal approximators

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

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 6,520 · see citing works

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759 · see citing works

A survey of deep neural network architectures and their applications

Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239 · see citing works

The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data

Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J., & Goetz, A. F. H. (1993) · Remote Sensing of Environment · doi:10.1016/0034-4257(93)90013-n · Cited by 3,193 · see citing works

Road Extraction by Deep Residual U-Net

Zhang, Z., Liu, Q., & Wang, Y. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2802944 · Cited by 3,179 · cited 1× · see citing works

Universal approximation bounds for superpositions of a sigmoidal function

Barron, A. R. (1993) · IEEE Transactions on Information Theory · doi:10.1109/18.256500 · Cited by 2,861 · see citing works

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

Navab, N., Joachim Hornegger, A., Wells, W. M., & Frangi, A. F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9 · Cited by 2,288 · see citing works

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Zhang, L., Zhang, L., & Du, B. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2540798 · Cited by 2,229 · see citing works

Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images

Lucien Wald, A., Ranchin, T., & Mangolini, M. (1997) · HAL · Cited by 1,362 · see citing works

Pansharpening by Convolutional Neural Networks

Masi, G., Cozzolino, D., Verdoliva, L., & Scarpa, G. (2016) · Remote Sensing · doi:10.3390/rs8070594 · Cited by 1,158 · see citing works

Recurrent convolutional neural network for object recognition

Liang, M., & Hu, X. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298958 · Cited by 1,013 · see citing works

A wavelet transform method to merge Landsat TM and SPOT panchromatic data

Zhou, J., Civco, D. L., & Silander, J. A. (1998) · International Journal of Remote Sensing · doi:10.1080/014311698215973 · Cited by 979 · see citing works

Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details

Liu, J. G. (2000) · International Journal of Remote Sensing · doi:10.1080/014311600750037499 · Cited by 977 · see citing works

A new look at IHS-like image fusion methods

Tu, T., Su, S.-C., Shyu, H.-C., & Huang, P. S. (2001) · Information Fusion · doi:10.1016/s1566-2535(01)00036-7 · Cited by 904 · see citing works

Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods

Otazu, X., González-Audícana, M., Fors, O., & Núñez, J. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2005.856106 · Cited by 848 · see citing works

Image fusion by a ratio of low-pass pyramid

Toet, A. (1989) · Pattern Recognition Letters · doi:10.1016/0167-8655(89)90003-2 · Cited by 699 · see citing works

Medical image retrieval using deep convolutional neural network

Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.025 · Cited by 433 · see citing works

Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network

Palsson, F., Sveinsson, J. R., & Ulfarsson, M. O. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2668299 · Cited by 364 · see citing works

First Steps Towards an Intelligent Laser Welding Architecture Using Deep Neural Networks and Reinforcement Learning

Günther, J., Pilarski, P. M., Helfrich, G., Shen, H., & Diepold, K. J. (2014) · Procedia Technology · doi:10.1016/j.protcy.2014.09.007 · Cited by 302 · see citing works

Multi-Language Online Handwriting Recognition

Keysers, D., Deselaers, T., Rowley, H. A., Wang, L.-L., & Cărbune, V. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572693 · Cited by 167 · see citing works

Deep neural networks with Elastic Rectified Linear Units for object recognition

Jiang, X., Pang, Y., Li, X., Pan, J. S., & Xie, Y. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.056 · Cited by 138 · see citing works

Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition

Lu, J., Wang, G., & Zhou, J. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713940 · Cited by 120 · see citing works

Deep Learning for Remote Sensing Image Understanding

Zhang, L., Xia, G.-S., Wu, T., Lin, L., & Tai, X. (2015) · Journal of Sensors · doi:10.1155/2016/7954154 · Cited by 97 · see citing works

Remote sensing image classification based on neural network ensemble algorithm

Han, M., Zhu, X., & Yao, W. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.04.044 · Cited by 72 · see citing works

A domain-adaptive two-stream U-Net for electron microscopy image segmentation

Bermúdez-Chacón, R., Márquez-Neila, P., Salzmann, M., & Fua, P. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363602 · Cited by 62 · see citing works

Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition

Lu, J., Wang, G., Deng, W., & Moulin, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_18 · Cited by 54 · see citing works

A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning

Rundo, L., Stefano, A., Militello, C., Russo, G. I., SABINI, M. G., D'Arrigo, C., Marletta, F., Ippolito, M., Mauri, G., Vitabile, S., et al. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.03.011 · Cited by 48 · see citing works

Convolutional networks with cross-layer neurons for image recognition

Zeng, Y., Li, T., Luo, G., Fujita, H., Yu, N., & Pan, Y. F. (2017) · Information Sciences · doi:10.1016/j.ins.2017.12.045 · Cited by 31 · see citing works

A fast and memory saved GPU acceleration algorithm of convolutional neural networks for target detection

Li, S., Dou, Y., Niu, X., Lv, Q., & Wang, Q. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.11.046 · Cited by 31 · see citing works

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Sun, A. Y., & Scanlon, B. R. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab1b7d · Cited by 556 · see citing works

A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods

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 · see citing works

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

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 289 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead

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 135 · see citing works

SD-UNet: Stripping down U-Net for Segmentation of Biomedical Images on Platforms with Low Computational Budgets

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 · see citing works

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 · see citing works

Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach

Doury, A., Somot, S., Gadat, S., Ribes, A., & Corre, L. (2022) · Climate Dynamics · doi:10.1007/s00382-022-06343-9 · Cited by 78 · see citing works

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 · see citing works

Convolutional neural networks with SegNet architecture applied to three-dimensional tomography of subsurface electrical resistivity: CNN-3D-ERT

Vu, M. T., & Jardani, A. (2021) · Geophysical Journal International · doi:10.1093/gji/ggab024 · Cited by 56 · see citing works

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 · see citing works

Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data With a U-Net and Curriculum Learning

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 · see citing works

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 · see citing works

Automatic velocity picking from semblances with a new deep-learning regression strategy: Comparison with a classification approach

Wang, W., McMechan, G. A., Ma, J., & Xie, F. (2020) · Geophysics · doi:10.1190/geo2020-0423.1 · Cited by 48 · see citing works

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 · see citing works

UPSNet: Unsupervised Pan-Sharpening Network With Registration Learning Between Panchromatic and Multi-Spectral Images

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 · see citing works

A part-scale, feature-based surrogate model for residual stresses in the laser powder bed fusion process

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 · see citing works

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 · see citing works

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 37 · see citing works

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 36 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Automated site-specific assessment of steel structures through integrating machine learning and fracture mechanics

Perry, B. J., Guo, Y., & Mahmoud, H. N. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104022 · Cited by 33 · see citing works

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 · see citing works

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 29 · see citing works

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 25 · see citing works

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 · see citing works

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 · see citing works

Band-Independent Encoder–Decoder Network for Pan-Sharpening of Remote Sensing Images

Liu, C., Zhang, Y., Wang, S.-G., Sun, M., Ou, Y., Wan, Y., & Liu, X. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2975230 · Cited by 18 · see citing works

R2D2-GAN: Robust Dual Discriminator Generative Adversarial Network for Microscopy Hyperspectral Image Super-Resolution

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 · see citing works

Ground-Motion Modeling as an Image Processing Task: Introducing a Neural Network Based, Fully Data-Driven, and Nonergodic Approach

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 23 · see citing works

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 21 · see citing works

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 21 · see citing works

Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging

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 · see citing works

A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning

Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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., & Vaz-de-Melo, P. O. S. (2024) · Earth-Science Reviews · doi:10.1016/j.earscirev.2024.104955 · Cited by 14 · see citing works

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 · see citing works

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 · see citing works

Detecting spiral wave tips using deep learning

Lilienkamp, H., & Lilienkamp, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-99069-3 · Cited by 13 · see citing works

Texture feed based convolutional neural network for pansharpening

Imani, M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.083 · Cited by 13 · see citing works

Tomato Leaf Disease Recognition via Optimizing Deep Learning Methods Considering Global Pixel Value Distribution

Li, Z., Tao, W., Liu, J., Zhu, F., Du, G., & Ji, G. (2023) · Horticulturae · doi:10.3390/horticulturae9091034 · Cited by 12 · see citing works

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 12 · see citing works

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 · see citing works

Coherence-Based Prediction of Multi-Temporal InSAR Measurement Availability for Infrastructure Monitoring

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 11 · see citing works

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 · see citing works

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 · see citing works

A machine learning approach for the efficient estimation of ground-level air temperature in urban areas

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 · see citing works

Convolutional neural network based proton stopping-power-ratio estimation with dual-energy CT: a feasibility study

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

HMFT: Hyperspectral and Multispectral Image Fusion Super‐Resolution Method Based on Efficient Transformer and Spatial‐Spectral Attention Mechanism

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

MIGDUN: Multi-stage interactive guidance deep unfolding network for pansharpening remote sensing images

Tao, H., Yuan, G., Li, H., & Li, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132527 · Cited by 6 · see citing works

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 11 · see citing works

A Deep Learning-Based Robust Change Detection Approach for Very High Resolution Remotely Sensed Images with Multiple Features

Huang, L., An, R., Zhao, S., Jiang, T., & Hu, H. (2020) · Remote Sensing · doi:10.3390/rs12091441 · Cited by 6 · see citing works

DanceCam: atmospheric turbulence mitigation in wide-field astronomical images with short-exposure video streams

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 · see citing works

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 · see citing works

A Knowledge Optimization-Driven Network With Normalizer-Free Group ResNet Prior for Remote Sensing Image Pan-Sharpening

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 · see citing works

A unified surrogate model for enhanced photon shielding through an all-natural-element multi-group flux dataset

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 · see citing works

Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression

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 · see citing works

AB-PLS-DA: Pansharpening tailored for scanning electron microscopy and energy-dispersive X-ray spectrometry multimodal fusion

Sihvonen, T., Duma, Z.-S., & Рейникайнен, С.-П. (2023) · Micron · doi:10.1016/j.micron.2023.103578 · Cited by 5 · see citing works

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 · see citing works

Deep Neural Network Regression for Normalized Digital Surface Model Generation With Sentinel-2 Imagery

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 · see citing works

Mapping Urban Water Stress and Human-Water Conflicts: a Climate–Ecosystem Framework for Sustainable Water Management

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 · see citing works

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 · see citing works

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 4 · see citing works

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 · see citing works

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 4 · see citing works

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 · see citing works

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 4 · see citing works

WP-UNet: Weight Pruning U-Net with Depthwise Separable Convolutions for Semantic Segmentation of Kidney TumorsPreprint

Rao, P. K., & Chatterjee, S. (2021) · Research Square · doi:10.21203/rs.3.rs-140504/v1 · Cited by 4 · see citing works

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 · see citing works

U-net for learning and inference of dense representation of multiple air pollutants from satellite imagery

Shelton, J. A., Polewski, P., & Yao, W. (2020) · International Workshop Climate Informatics · doi:10.1145/3429309.3429328 · Cited by 3 · see citing works

Residual 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 · see citing works

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 · see citing works

Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope ImagesPreprint

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Automated detection of ferromagnetic pipelines from magnetic total-field anomaly data using convolutional neural networks

Bernstein, B., Li, Y., & Hammack, R. (2023) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/image2023-3912400.1 · Cited by 2 · see citing works

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 · see citing works

Bed Topography Inference from Velocity Field Using Deep Learning

Oshtorjani, M. K., & Ancey, C. (2023) · Water · doi:10.3390/w15234055 · Cited by 2 · see citing works

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 · see citing works

Synthesis of MR fingerprinting information from magnitude-only MR imaging data using a parallelized, multi network U-Net convolutional neural network

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 · see citing works

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 · see citing works

Pansharpening by Convolutional Neural Networks

Masi, G., Cozzolino, D., Verdoliva, L., & Scarpa, G. (2016) · Remote Sensing · doi:10.3390/rs8070594 · Cited by 1,158 · see citing works

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 · see citing works

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 106 · see citing works

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 · see citing works

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 · see citing works