| Active deep learning for classification of hyperspectral images HKBE peng liu IEEE JSTARS 10 (2), 712 - 724, 2017 | 308* | 2017 |
| SVM or deep learning? A comparative study on remote sensing image classification P Liu, KKR Choo, L Wang, F Huang Soft Computing 21, 7053-7065, 2017 | 277 | 2017 |
| Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification L Wang, J Zhang, P Liu, KKR Choo, F Huang Soft Computing 21, 213-221, 2017 | 174 | 2017 |
| A survey of remote-sensing big data P Liu frontiers in Environmental Science 3, 45, 2015 | 142 | 2015 |
| Air quality predictions with a semi-supervised bidirectional LSTM neural network L Zhang, P Liu, L Zhao, G Wang, W Zhang, J Liu Atmospheric Pollution Research 12 (1), 328-339, 2021 | 118 | 2021 |
| Particle swarm optimization based dictionary learning for remote sensing big data L Wang, H Geng, P Liu, K Lu, J Kolodziej, R Ranjan, AY Zomaya Knowledge-Based Systems 79, 43-50, 2015 | 96 | 2015 |
| IK-SVD: dictionary learning for spatial big data via incremental atom update L Wang, K Lu, P Liu, R Ranjan, L Chen Computing in Science & Engineering 16 (4), 41-52, 2014 | 94 | 2014 |
| Remote-sensing image denoising using partial differential equations and auxiliary images as priors P Liu, F Huang, G Li, Z Liu IEEE Geoscience and Remote Sensing Letters 9 (3), 358-362, 2012 | 93 | 2012 |
| A Survey on Active Deep Learning: From Model-driven to Data-driven P Liu, L Wang, R RANJAN, G He, L Zhao ACM Computing Surveys, 2022 | 92 | 2022 |
| Towards building a data-intensive index for big data computing–A case study of Remote Sensing data processing Y Ma, L Wang, P Liu, R Ranjan Information Sciences 319, 171-188, 2015 | 88 | 2015 |
| Remote sensing big data: Theory, methods and applications P Liu, L Di, Q Du, L Wang Remote Sensing 10 (5), 711, 2018 | 71 | 2018 |
| Spatiotemporal fusion of MODIS and Landsat-7 reflectance images via compressed sensing J Wei, L Wang, P Liu, X Chen, W Li, AY Zomaya IEEE Transactions on Geoscience and Remote Sensing 55 (12), 7126-7139, 2017 | 70 | 2017 |
| Link the remote sensing big data to the image features via wavelet transformation L Wang, W Song, P Liu Cluster Computing 19, 793-810, 2016 | 70 | 2016 |
| Compressed sensing of a remote sensing image based on the priors of the reference image L Wang, K Lu, P Liu IEEE Geoscience and Remote Sensing Letters 12 (4), 736-740, 2014 | 70 | 2014 |
| CycleGAN-STF: Spatiotemporal fusion via CycleGAN-based image generation J Chen, L Wang, R Feng, P Liu, W Han, X Chen IEEE Transactions on Geoscience and Remote Sensing 59 (7), 5851-5865, 2020 | 60 | 2020 |
| Parallel programing templates for remote sensing image processing on GPU architectures: design and implementation Y Ma, L Chen, P Liu, K Lu Computing 98, 7-33, 2016 | 59 | 2016 |
| Spatiotemporal fusion of remote sensing images with structural sparsity and semi-coupled dictionary learning J Wei, L Wang, P Liu, W Song Remote Sensing 9 (1), 21, 2016 | 51 | 2016 |
| Remote sensing data fusion with generative adversarial networks: State-of-the-art methods and future research directions P Liu, J Li, L Wang, G He IEEE Geoscience and Remote Sensing Magazine 10 (2), 295-328, 2022 | 45* | 2022 |
| Sample generation based on a supervised Wasserstein Generative Adversarial Network for high-resolution remote-sensing scene classification W Han, L Wang, R Feng, L Gao, X Chen, Z Deng, J Chen, P Liu Information Sciences 539, 177-194, 2020 | 44 | 2020 |
| MLFF-GAN: A multilevel feature fusion with GAN for spatiotemporal remote sensing images B Song, P Liu, J Li, L Wang, L Zhang, G He, L Chen, J Liu IEEE Transactions on Geoscience and Remote Sensing 60, 1-16, 2022 | 39 | 2022 |
