The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
Open accessAbstract
Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multispectral (MS) images. As the transformation from low spatial resolution MS image to high-resolution MS image is complex and highly nonlinear, inspired by the powerful representation for nonlinear relationships of deep neural networks, we introduce multiscale feature extraction and residual learning into the basic convolutional neural network (CNN) architecture and propose the multiscale and multidepth CNN for the pan-sharpening of remote sensing imagery. Both the quantitative assessment results and the visual assessment confirm that the proposed network yields high-resolution MS images that are superior to the images produced by the compared state-of-the-art methods.
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
“Also, the multi-path CNN structures can fuse the extracted features in multiple depth of layers [22].”
From Automated placental abruption identification using semantic segmentation, quantitative features, SVM, ensemble and multi-path CNN (2023) · cites this paper as [22]
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
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
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
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
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2662206 · Cited by 8,983
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
A universal image quality index
Wang, Z., & Bovik, A. C. (2002) · IEEE Signal Processing Letters · doi:10.1109/97.995823 · Cited by 5,757
DehazeNet: An End-to-End System for Single Image Haze Removal
Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2598681 · Cited by 3,367
Deeply-Recursive Convolutional Network for Image Super-Resolution
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 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.2016.181 · Cited by 3,158
Research works printed but not linked: 1
- 2015, ACM Int Conf Multimedia, 689
Cited by
Sorted by citations
Items 301–400 of 649
Song, A., & Kim, Y. (2020) · Remote Sensing · doi:10.3390/rs12071099 · Cited by 10
A deep image prior-based interpretable network for hyperspectral image fusion
Sun, Y., Liu, J., Yang, J., Xiao, Z., & Wu, Z. (2021) · Remote Sensing Letters · doi:10.1080/2150704x.2021.1979270 · Cited by 10
Bao, K., Meng, X., Chai, X., & Shao, F. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3177607 · Cited by 10
Self-citationMTF driven adaptive multiscale bilateral filtering for pansharpening
Kaplan, N. H., Erer, I., Özcan, O., & Musaoğlu, N. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2019.1590874 · Cited by 10
Zhang, G., Liang, S., Ma, H., He, T., Yin, G., Xu, J., Liu, X., & Zhang, Y. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113928 · Cited by 9
Alexiou, M. S., & Mertoguno, J. S. (2023) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai59109.2023.00047 · Cited by 11
Deep Variational Network for Blind Pansharpening
Zhang, Z. Y., Li, H., Ke, C., Chen, J., & Tian, X. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3436850 · Cited by 9
Adaptive Multiscale Slimming Network Learning for Remote Sensing Image Feature Extraction
Ye, D., Peng, J., Guo, W., & Li, H. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3490666 · Cited by 9
Wang, S., Wang, Y., Hong, D., & Zhang, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3514893 · Cited by 9
Self-citationRTDU: Interpretable Region-Aware Transformer-Based Deep Unfolding Network for Pan-Sharpening
Wang, S., Yuan, G., & Li, J. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3567769 · Cited by 9
Transformer gate-based interactive U-Net for hyperspectral and multispectral image fusion
Yu, K., Fu, Y., Liu, L., Wang, J., & Peng, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128278 · Cited by 9
Chen, Y., & Ge, Y. (2022) · Science of Remote Sensing · doi:10.1016/j.srs.2022.100062 · Cited by 9
Agrawal, A., SourajaKundu, Ahmad, T., & Bhatt, M. (2024) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.24-00003r2 · Cited by 9
Lu, X., Yang, D., Jia, F., & Zhao, Y. (2020) · Applied Sciences · doi:10.3390/app11010288 · Cited by 9
SARF: A Simple, Adjustable, and Robust Fusion Method
Meng, X., Yang, G., Shao, F., Sun, W., Shen, H., & Shutao Li, A. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3060095 · Cited by 9
Self-citationSmadi, A. A., Yang, S., Abugabah, A., AlZubi, A. A., & Sanzogni, L. (2022) · IEEE Access · doi:10.1109/access.2022.3169698 · Cited by 9
Spectral-Spatial Interaction Network for Multispectral Image and Panchromatic Image Fusion
Nie, Z., Chen, L., Jeon, S., & Yang, X. (2022) · Remote Sensing · doi:10.3390/rs14164100 · Cited by 9
Modality-Aware Feature Integration for Pan-Sharpening
Zhou, M., Huang, J., Zhao, F., & Hong, D. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3232384 · Cited by 9
Deep unfolding for hyper sharpening using a high-frequency injection module
Mifdal, J., Tomás-Cruz, M., Sebastianelli, A., Coll, B., & Duran, J. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00204 · Cited by 9
Rethinking the Role of Panchromatic Images in Pan-Sharpening
Wang, J., Chen, X., Huang, X., Zhang, R., Wang, Y., & Lü, T. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3535309 · Cited by 8
Contour-Aware Dynamic Low-High Frequency Integration for Pan-Sharpening
Yi, X., Zhu, H., Guo, P., Hou, B., Ren, B., Li, X., Wang, X., & Jiao, L. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3547419 · Cited by 8
Panchromatic Side Sparsity Model-Based Deep Unfolding Network for Pansharpening
Yin, H. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3139190 · Cited by 8
Vector-Quantized Variational AutoEncoder for pansharpening
Talbi, F., El-Mezouar, M. C., Boutellaa, E., & Alim, F. (2023) · International Journal of Remote Sensing · doi:10.1080/01431161.2023.2265542 · Cited by 8
Shen, H., Zhang, B., Jiang, M., & Li, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3422896 · Cited by 8
Self-citationHeterogeneous Network-Based Contrastive Learning Method for PolSAR Land Cover Classification
Cai, J., Ma, Y., Feng, Z., Yang, S., & Jiao, L. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3429538 · Cited by 10
High-Fidelity Pansharpening via Trigeminal Pyramid Decoding of CNN-Transformer Encoded Features
Chen, L., Song, T., Jian, L., Zhang, D., Vivone, G., & Zhou, X. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3582642 · Cited by 9
FrMLNet: Framelet-Based Multilevel Network for Pansharpening
Wang, T., Fang, F., Zheng, H., & Zhang, G. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3131651 · Cited by 8
RFSDAF: A New Spatiotemporal Fusion Method Robust to Registration Errors
Hou, S., Sun, W., Guo, B., Li, X., Zhang, J., Xu, C., Li, X., Shao, Y., & Li, C. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3138078 · Cited by 8
Spectral-Spatial Residual Network for Fusing Hyperspectral and Panchromatic Remote Sensing Images
Zhao, R., & Du, S. (2022) · Remote Sensing · doi:10.3390/rs14030800 · Cited by 8
Multispectral Pansharpening Based on Multisequence Convolutional Recurrent Neural Network
Wang, P., He, Z., Zhang, Y., Zhang, G., Liu, H., & Leung, H. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3218367 · Cited by 8
Application of SSD network algorithm in panoramic video image vehicle detection system
Jiang, T. (2023) · Open Computer Science · doi:10.1515/comp-2022-0270 · Cited by 8
MPEFNet: Multilevel Progressive Enhancement Fusion Network for Pansharpening
He, L., Nie, R., Cao, J., Jin, B., & Han, Y. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3298995 · Cited by 8
Remote Sensing Pan-Sharpening via Cross-Spectral–Spatial Fusion Network
Wang, Y., Shao, Z., Lü, T., Wang, J., Cheng, G., Zuo, X., & Dang, C. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3337844 · Cited by 8
Zhang, J., Zhang, D., Ma, W., & Jiao, L. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2854847 · Cited by 8
Learning an Efficient Convolution Neural Network for Pansharpening
Guo, Y., Ye, F., & Gong, H. (2019) · Algorithms · doi:10.3390/a12010016 · Cited by 8
Tang, T., Chen, T., Chen, X., & Gui, G. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3441550 · Cited by 7
Supervised Detail-Guided Multiscale State-Space Model for Pan-Sharpening
Zhao, M., Ma, M., Li, X., Ma, X., Zhang, W., & Song, S. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3519371 · Cited by 7
Wang, Y., Lin, Y., He, X., Zheng, H., Yan, K., Fan, L., Huang, Y., & Ding, X. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3538744 · Cited by 7
A pan-sharpening model using dual-branch attention-guided diffusion networks
Zheng, H., Pan, C., Jin, X., Woźniak, M., Wang, P., Lee, S., & Jiang, Q. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103076 · Cited by 9
Dual-Level Cross-Modality Neural Architecture Search for Guided Image Super-Resolution
Zhong, Z., Liu, X., Jiang, J., Zhao, D., & Wang, S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3578468 · Cited by 7
Multi-scale adaptive weighted network for polarization computational imaging super-resolution
Xu, G., Wang, J., Zhang, L., Ma, J., Wang, Y., & Liu, J. (2022) · Applied Physics B · doi:10.1007/s00340-022-07900-0 · Cited by 7
Self-citationRobust Prediction of Sea Surface Temperature Based on SSPGAN
Yao, X., Zheng, G., Yu, J., Shao, J., & Zuo, J. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3316731 · Cited by 7
AMP-BCS: AMP-based image block compressed sensing with permutation of sparsified DCT coefficients
Li, J., Hou, X., Wang, H., Bi, S., & Qian, X. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104092 · Cited by 7
Deep Learning Hyperspectral Pansharpening on Large-Scale PRISMA Dataset
Zini, S., Barbato, M. P., Piccoli, F., & Napoletano, P. (2024) · Remote Sensing · doi:10.3390/rs16122079 · Cited by 7
CDSS-Pan: Convolutional Dictionary Learning With State Space Model for Pansharpening
Li, Z., Song, J., Yuan, G., & Li, J. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3510545 · Cited by 7
FrDiff: Framelet-Based Conditional Diffusion Model for Multispectral and Panchromatic Image Fusion
Zhang, J., Fang, F., Wang, T., Zhang, G., & Song, H. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3565985 · Cited by 7
He, J., Li, J., Yuan, Q., Li, H., & Shen, H. (2019) · Remote Sensing · doi:10.3390/rs11192203 · Cited by 7
Self-citationDetail Information Prior Net for Remote Sensing Image Pansharpening
Xie, Y., Wu, W., Yang, H., Wu, N., & Shen, Y. (2021) · Remote Sensing · doi:10.3390/rs13142800 · Cited by 7
Attention-Based Multistage Fusion Network for Remote Sensing Image Pansharpening
Zhang, W., Li, J., & Hua, Z. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3113984 · Cited by 7
Cross-Domain Association Mining Based Generative Adversarial Network for Pansharpening
He, L., Zhang, W., Shi, J., & Li, F. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3204824 · Cited by 7
Dual-Branch Multi-Level Feature Aggregation Network for Pansharpening
Cheng, G., Shao, Z., Wang, J., Huang, X., & Dang, C. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105956 · Cited by 7
An Efficient Cross-Modality Self-Calibrated Network for Hyperspectral and Multispectral Image Fusion
Wu, H., Gui, J., Xu, Y., Wu, Z., Tang, Y. Y., & Wei, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3225577 · Cited by 7
Hyperspectral Image Fusion Algorithm Based on Improved Deep Residual Network
Li, Y., Fu, M., Zhang, H., Xu, H., & Zhang, Q. (2023) · Signal Processing · doi:10.1016/j.sigpro.2023.109058 · 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
Yılmaz, V. (2023) · International Journal of Remote Sensing · doi:10.1080/01431161.2023.2234095 · Cited by 7
A Joint Optimization Based Pansharpening via Subpixel-Shift Decomposition
Han, X., Leng, W., Xu, Q., Li, W., Tao, R., & Sun, W. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3329736 · Cited by 7
Neural network in sports cluster analysis
Zhang, Y., Hou, X., & Xu, S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05585-0 · Cited by 7
Hyperspectral Super-Resolution Via Joint Regularization of Low-Rank Tensor Decomposition
Cao, M., Bao, W., & Qu, K. (2021) · Remote Sensing · doi:10.3390/rs13204116 · Cited by 7
Li, R., Zhang, L., Wang, Z., & Li, X. (2024) · Sensors · doi:10.3390/s24217023 · Cited by 6
Self-citationMu, K., Wang, W., Gao, M., & Liu, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111959 · Cited by 6
Kumar, R., Roy, S. K., Vivone, G., Das, S., Raman, B., & Singh, P. P. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103834 · Cited by 6
Tao, H., Yuan, G., Li, H., & Li, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132527 · Cited by 6
Enpowering Your Pansharpening Models with Generalizability: Unified Distribution Is All You Need
Cui, Y., Liu, P., & Zhang, H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01102 · Cited by 6
GRF: guided residual fusion for pansharpening
Li, J., Ai, M., Wang, S., & Hu, Q. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2100726 · Cited by 6
A Multitask Network for Joint Multispectral Pansharpening on Diverse Satellite Data
Wang, D., Wu, C., Bai, Y., Li, Y., Shang, C., & Shen, Q. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3306896 · Cited by 6
CTCP: Cross Transformer and CNN for Pansharpening
Su, Z. Y., Yang, Y., Huang, S., Wan, W., Tu, W., Lu, H., & Chen, C. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3613815 · Cited by 6
Coupled Matrix Factorization Constrained Deep Hyperspectral and Multispectral Image Fusion
Wu, X., Xiao, S., Dong, W., Qu, J., Zhang, T., & Li, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3347564 · Cited by 6
Neural network big data fusion in remote sensing image processing technology
Wu, X. (2024) · Journal of Intelligent Systems · doi:10.1515/jisys-2023-0147 · Cited by 6
Zhang, Z., Ao, Z., Wu, W., Wang, Y., & Xin, Q. (2024) · Remote Sensing · doi:10.3390/rs16061086 · Cited by 6
Multi-Frequency Spectral–Spatial Interactive Enhancement Fusion Network for Pan-Sharpening
Tang, Y., Li, H., Xie, G., Liu, P., & Li, T. (2024) · Electronics · doi:10.3390/electronics13142802 · Cited by 6
Kernel-guided injection deep network for blind fusion of multispectral and panchromatic images
Ke, C., Zhang, Z. Y., Zhang, W., Chen, J., & Tian, X. S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110889 · Cited by 6
Progressive Reconstruction Network With Adaptive Frequency Adjustment for Pansharpening
Zhao, X., Zhang, Y., Guo, J., Zhu, Y., Zhou, G., Zhang, W., & Wu, Y. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3452311 · Cited by 6
Shuffle Mamba: State Space Models With Random Shuffle for Multi-Modal Image Fusion
Cao, K., He, X., Hu, T., Xie, C., Zhou, M., & Zhang, J. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3668923 · Cited by 6
Liu, X., Tang, P. T. P., Jin, X., & Zhang, Z. (2022) · Remote Sensing · doi:10.3390/rs14051242 · Cited by 6
Zhao, R., & Du, S. (2022) · Remote Sensing · doi:10.3390/rs14091981 · Cited by 6
MSAC-Net: 3D Multi-Scale Attention Convolutional Network for Multi-Spectral Imagery Pansharpening
Zhang, E., Fu, Y., Wang, J., Liu, L., Yu, K., & Peng, J. (2022) · Remote Sensing · doi:10.3390/rs14122761 · Cited by 6
STFRDN: a residual dense network for remote sensing image spatiotemporal fusion
Erdem, F., & Avdan, U. (2023) · International Journal of Remote Sensing · doi:10.1080/01431161.2023.2221800 · Cited by 6
Dynamic Large-Small Kernel Convolutional Neural Network for Pansharpening
Feng, X. L., Hu, J., Wu, W., & Fan, S. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3307025 · Cited by 6
Liu, Z., Han, G., Yang, H., Liu, P., Chen, D., Liu, D., & Deng, A. (2023) · Remote Sensing · doi:10.3390/rs15174328 · Cited by 6
A Deep Learning-Based Spatio-Temporal NDVI Data Fusion Model
Ziyu, S., Ouyang, X., Li, H., & Junbang, W. (2024) · Journal of Resources and Ecology · doi:10.5814/j.issn.1674-764x.2024.01.019 · Cited by 9
A Deep Error Removal Network for Pan-Sharpening
Wang, J., Lü, T., Huang, X., Zhang, R., & Luo, D. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3454124 · Cited by 6
Overcoming detector limitations of x-ray photon counting for preclinical microcomputed tomography
Holbrook, M., Clark, D. P., & Badea, C. T. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.6.1.011004 · Cited by 6
Lai, Z., Chen, L., Liu, Z., & Yang, X. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1945703 · Cited by 6
Multi-Supervised Recursive-CNN for Hyperspectral and Multispectral Image Fusion
Lu, Y., Yang, J., & Xiao, L. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9553598 · 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
Self-citationLuo, J., Zhou, F., Yang, J., & Xing, M. (2023) · Remote Sensing · doi:10.3390/rs15123091 · Cited by 6
A pan-sharpening method based on the ADMM algorithm
Chen, Y., Wang, T., Fang, F., & Zhang, G. (2019) · Frontiers of Earth Science · doi:10.1007/s11707-019-0754-z · Cited by 6
Remote sensing image fusion based on Bayesian GANPreprint
Chen, J., Pan, Y., & Yang, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.09465 · Cited by 6
A Novel Spectral-Spatial Attention Network for Zero-Shot Pansharpening
Lu, H., Paoletti, M. E., Haut, J. M., Moreno‐Álvarez, S., Chen, G., & Jing, W. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3587244 · Cited by 5
Toward Resolution Mismatching: Modality-Aware Feature-Aligned Network for Pan-Sharpening
Zhou, M., He, X., & Hong, D. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3594898 · Cited by 5
Equivariant Bayesian Hyperspectral Imaging via Mosaiced and PAN Image Fusion
Dian, R., Wang, N., Guo, A., & Shutao Li, A. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3682543 · Cited by 5
Modified Dynamic Routing Convolutional Neural Network for Pan-Sharpening
Sun, K., Zhang, J., Liu, J., Xu, S., Cao, X., & Fei, R. (2023) · Remote Sensing · doi:10.3390/rs15112869 · Cited by 6
Wang, J., Miao, J., Li, G., Tan, Y. H. V., Yu, S., Liu, X., Zeng, L., & Li, G. (2023) · Remote Sensing · doi:10.3390/rs16010075 · Cited by 5
Wang, W., Liu, H., & Xie, G. (2021) · Sensors · doi:10.3390/s21113586 · Cited by 5
Jin, W., Wang, M., Wang, W., & Yang, G. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3314085 · Cited by 5
DMCSC: Deep Multisource Convolutional Sparse Coding Model for Pansharpening
Zhang, J., Ye, Y., Fang, F., Wang, T., & Zhang, G. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3329150 · Cited by 5
Mulay, S., Ram, K., & Sivaprakasam, M. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05033-1 · Cited by 5
Ge, Z.-Z., Ding, Z., Wang, Y., Bian, L.-F., & Yang, C. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104180 · Cited by 5
Liu, Y., Li, J., He, Q., & Yang, B. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3478269 · Cited by 5
Pan-sharpening via Symmetric Multi-Scale Correction-Enhancement Transformers
Li, Y. H., Wang, Y., Shi, S., Wang, J., Wang, R., Lu, M., & Zhang, F. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107226 · Cited by 5
Metadata
- Authors
- Yuan, Q., Wei, Y., Meng, X., Shen, H., & Zhang, L.
- Affiliations
- Wuhan University (CN)
- Ningbo University (CN)
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2018-03
- Volume
- 11
- Issue
- 3
- Pages
- 978-989
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2963183385
- Cited by
- 649
- References
- 54
- Citations by year
- 2026: 70 · 2025: 86 · 2024: 103 · 2023: 95 · 2022: 96 · 2021: 84 · 2020: 60 · 2019: 40 · 2018: 15
- FWCI
- 37.70
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2016YFB0501403)
- National Natural Science Foundation of China (41431175)
- Fundamental Research Funds for the Central Universities (2042017kf0180)
- Natural Science Foundation of Hubei Province (ZRMS2016000241)
- Topics
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Satellite Image Processing and Photogrammetry
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- pansharpening, multispectral imagery, panchromatic imagery, multi-scale feature extraction, residual learning, spatial resolution enhancement
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