Deep learning for processing and analysis of remote sensing big data: a technical review

References

• Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., & Alamri, A. (2020). Deep learning approaches applied to remote sensing datasets for road extraction: A state-of-the-art review. Remote Sensing, 12(9), 1444.
   Web of Science ®Google Scholar
• Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.
   Web of Science ®Google Scholar
• Benzenati, T., Kallel, A., & Kessentini, Y. (2020). Two stages pan-sharpening details injection approach based on very deep residual networks. IEEE Transactions on Geoscience and Remote Sensing, 59(6), 4984–4992.
   Web of Science ®Google Scholar
• Bermudez, J. D., Happ, P. N., Feitosa, R. Q., & Oliveira, D. A. B. (2019). Synthesis of multispectral optical images from SAR/optical multitemporal data using conditional generative adversarial networks. IEEE Geoscience and Remote Sensing Letters, 16(8), 1220–1224.
   Web of Science ®Google Scholar
• Brandt, M., Tucker, C. J., Kariryaa, A., Rasmussen, K., Abel, C., Small, J., … Fensholt, R. (2020). An unexpectedly large count of trees in the West African Sahara and Sahel. Nature, 587(7832), 78–82.
   PubMed Web of Science ®Google Scholar
• Cao, R., Tu, W., Yang, C., Li, Q., Liu, J., Zhu, J., … Qiu, G. (2020). Deep learning-based remote and social sensing data fusion for urban region function recognition. ISPRS Journal of Photogrammetry and Remote Sensing, 163, 82–97.
   Web of Science ®Google Scholar
• Censi, A. M., Ienco, D., Gbodjo, Y. J. E., Pensa, R. G., Interdonato, R., & Gaetano, R. (2021). Attentive spatial temporal graph CNN for land cover mapping from multi temporal remote sensing data. IEEE Access, 9, 23070–23082.
   Web of Science ®Google Scholar
• Chen, X., Xiang, S., Liu, C. L., & Pan, C.-H. (2014). Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 11(10), 1797–1801.
   Web of Science ®Google Scholar
• Chen, Y., Fan, R., Yang, X., Wang, J., & Latif, A. (2018). Extraction of urban water bodies from high-resolution remote-sensing imagery using deep learning. Water, 10(5), 585.
   Web of Science ®Google Scholar
• Chen, Z., Zhang, T., & Ouyang, C. (2018). End-to-end airplane detection using transfer learning in remote sensing images. Remote Sensing, 10(1), 139.
   Web of Science ®Google Scholar
• Cheng, G., Han, J., Zhou, P., & Xu, D. (2019). Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection. IEEE Transactions on Image Processing, 28(1), 265–278.
   PubMed Web of Science ®Google Scholar
• Cheng, G., Zhou, P., & Han, J. (2016). Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7405–7415.
   Web of Science ®Google Scholar
• Chung, J., Gulcehre, C., Cho, K. H. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
   Google Scholar
• Cui, W., He, X., Yao, M., Wang, Z., Hao, Y., Li, J., … Cui, W. (2021). Knowledge and spatial pyramid distance-based gated graph attention network for remote sensing semantic segmentation. Remote Sensing, 13(7), 1312.
   Web of Science ®Google Scholar
• Dadsetan, S., Pichler, D., Wilson, D., Hovakimyan. N., & Hobbs. J. (2021). Superpixels and graph convolutional neural networks for efficient detection of nutrient deficiency stress from aerial imagery. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 2950–2959.
   Google Scholar
• Das, M., & Ghosh, S. K. (2016). Deep-STEP: A deep learning approach for spatiotemporal prediction of remote sensing data. IEEE Geoscience and Remote Sensing Letters, 13(12), 1984–1988.
   Web of Science ®Google Scholar
• Deng, Z., Sun, H., Zhou, S., Zhao, J., Lei, L., & Zou, H. (2018). Multi-scale object detection in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 3–22.
   Web of Science ®Google Scholar
• Ding, J., Xue, N., Long, Y., Xia, G., & Lu, Q. (2018). Learning roi transformer for detecting oriented objects in aerial images. arXiv preprint arXiv:1812.00155.
   Google Scholar
• Dong, G., Liao, G., Liu, H., & Kuang, G. (2018). A review of the autoencoder and its variants: A comparative perspective from target recognition in synthetic-aperture radar images. IEEE Geoscience and Remote Sensing Magazine, 6(3), 44–68.
   Web of Science ®Google Scholar
• Du, B., Ru, L., Wu, C., & Zhang, L. (2019). Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(12), 9976–9992.
   Web of Science ®Google Scholar
• Duan, W., Chiang, Y. Y., Leyk, S., Uhl, J. H., & Knoblock, C. A. (2020). Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning. International Journal of Geographical Information Science, 34(4), 824–849.
   Web of Science ®Google Scholar
• Duffy, K. M., Vandal, T., Li, S., Ganguly, S., Nemani, R., & Ganguly, A. R. (2019). DeepEmSat: Deep emulation for satellite data mining. Frontiers in Big Data, 2, 42.
   Google Scholar
• Feng, J., Wu, X., Chen, J., Zhang, X., Tang, X., & Li, D. (2019). Joint multilayer spatial-spectral classification of hyperspectral images based on CNN and ConvLSTM. IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019a: 588–591.
   Google Scholar
• Feng, Q., Yang, J., Liu, Y., Ou, C., Zhu, D., Niu, B., … Li, B. (2020). Multi-temporal unmanned aerial vehicle remote sensing for vegetable mapping using an attention-based recurrent convolutional neural network. Remote Sensing, 12(10), 1668.
   Web of Science ®Google Scholar
• Feng, Q., Zhu, D., Yang, J., & Li, B. (2019b). Multisource hyperspectral and lidar data fusion for urban land-use mapping based on a modified two-branch convolutional neural network. ISPRS International Journal of Geo-Information, 8(1), 28.
   Web of Science ®Google Scholar
• Feng, W., Sui, H., Huang, W., Xu, C., & An, K. (2018). Water body extraction from very high-resolution remote sensing imagery using deep U-Net and a superpixel-based conditional random field model. IEEE Geoscience and Remote Sensing Letters, 16(4), 618–622.
   Web of Science ®Google Scholar
• Fu, T., Ma, L., Li, M., & Johnson, B. A. (2018). Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery. Journal of Applied Remote Sensing, 12(2), 025010.
   Web of Science ®Google Scholar
• Galar, M., Sesma, R., Ayala, C., Albizua, L., & Aranda, C. (2020). Super-resolution of Sentinel-2 images using convolutional neural networks and real ground truth data. Remote Sensing, 12(18), 2941.
   Web of Science ®Google Scholar
• Gao, F., Dong, J., Li, B., & Xu, Q. (2016). Automatic change detection in synthetic aperture radar images based on PCANet. IEEE Geoscience and Remote Sensing Letters, 13(12), 1792–1796.
   Web of Science ®Google Scholar
• Gao, X., Sun, X., Zhang, Y., Yan, M., Xu, G., Sun, H., … Fu, K. (2018). An end-to-end neural network for road extraction from remote sensing imagery by multiple feature pyramid network. IEEE Access, 6, 39401–39414.
   Web of Science ®Google Scholar
• Gao, Y., Shi, J., & Wang, R., (2021). Remote sensing scene classification based on high-order graph convolutional network. European Journal of Remote Sensing, 141–155.
   Web of Science ®Google Scholar
• Gers, F. A., Schraudolph, N. N., & Schmidhuber, J. (2002). Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research, 3(Aug), 115–143.
   Google Scholar
• Girard, N., Charpiat, G., & Tarabalka, Y. Aligning and updating cadaster maps with aerial images by multi-task, multi-resolution deep learning. Asian Conference on Computer Vision. Springer, Cham, 2018: 675–690.
   Google Scholar
• Gong, M., Yang, H., & Zhang, P. (2017). Feature learning and change feature classification based on deep learning for ternary change detection in SAR images. ISPRS Journal of Photogrammetry and Remote Sensing, 129, 212–225.
   Web of Science ®Google Scholar
• Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 2672–2680.
   Google Scholar
• Graves, A., Mohamed, A., & Hinton, G. Speech recognition with deep recurrent neural networks. (2013). IEEE international conference on acoustics, speech and signal processing. IEEE, 2013: 6645–6649.Vancouver, Canada.
   Google Scholar
• Grohnfeldt, C., Schmitt, M., & Zhu, X. A conditional generative adversarial network to fuse sar and multispectral optical data for cloud removal from Sentinel-2 images. IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018: 1726–1729.Valencia, Spain.
   Google Scholar
• Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2020). A review on generative adversarial networks: Algorithms, theory, and applications. arXiv preprint arXiv:2001.06937.
   Google Scholar
• Guirado, E., Tabik, S., Rivas, M. L., Alcaraz-Segura, D., & Herrera, F. (2019). Whale counting in satellite and aerial images with deep learning. Scientific Reports, 9(1), 1–12.
   Google Scholar
• Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019). Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 922–929.
   Google Scholar
• Halefom, A., Teshome, A., Sisay, E., & Ahmad, I. (2018). Dynamics of land use and land cover change using remote sensing and GIS: A case study of Debre Tabor Town, South Gondar, Ethiopia. Journal of Geographic Information System, 10(2), 165.
   Google Scholar
• Haut, J. M., Fernandez-Beltran, R., Paoletti, M. E., Plaza, J., Plaza, A., & Pla, F. (2018). A new deep generative network for unsupervised remote sensing single-image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 56(11), 6792–6810.
   Web of Science ®Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770–778.
   Google Scholar
• He, W., & Yokoya, N. (2018). Multi-temporal sentinel-1 and-2 data fusion for optical image simulation. ISPRS International Journal of Geo-Information, 7(10), 389.
   Web of Science ®Google Scholar
• Hewage, P., Behera, A., Trovati, M., Pereira, E., Ghahremani, M., Palmieri, F., & Liu, Y. (2020). Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station. Soft Computing, 1–30.
   Web of Science ®Google Scholar
• Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.
   PubMed Web of Science ®Google Scholar
• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
   PubMed Web of Science ®Google Scholar
• Hou, X., Wang, B., Hu, W., Yin, L., & Wu, H. (2019). SolarNet: A deep learning framework to map solar power plants in China from satellite imagery. arXiv preprint arXiv:1912.03685.
   Google Scholar
• Hu, W., Li, H., Pan, L., Li, W., Tao, R., & Du, Q. (2019a). Feature extraction and classification based on spatial-spectral convlstm neural network for hyperspectral images. arXiv preprint arXiv:1905.03577.
   Google Scholar
• Hu, W. S., Li, H. C., Pan, L., Li, W., Tao, R., & Du, Q. (2020). Spatial–spectral feature extraction via deep ConvLSTM neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(6), 4237–4250.
   Web of Science ®Google Scholar
• Hu, Y., Li, X., Zhou, N., Yang, L., & Peng, L. (2019b). A sample update-based convolutional neural network framework for object detection in large-area remote sensing images. IEEE Geoscience and Remote Sensing Letters, 16(6), 947–951.
   Web of Science ®Google Scholar
• Huang, B., Zhao, B., & Song, Y. (2018). Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment, 214, 73–86.
   Web of Science ®Google Scholar
• Huang, J., Zhang, X., Xin, Q., Sun, Y., & Zhang, P. (2019). Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 91–105.
   Web of Science ®Google Scholar
• Huang, W., Xiao, L., Wei, Z., Liu, H., & Tang, S. (2015). A new pan-sharpening method with deep neural networks. IEEE Geoscience and Remote Sensing Letters, 12(5), 1037–1041.
   Web of Science ®Google Scholar
• Hughes, L. H., Schmitt, M., & Zhu, X. X. (2018). Mining hard negative samples for SAR-optical image matching using generative adversarial networks. Remote Sensing, 10(10), 1552.
   Web of Science ®Google Scholar
• Hui, J., Du, M., Ye, X., Qin, Q., & Sui, J. (2018). Effective building extraction from high-resolution remote sensing images with multitask driven deep neural network. IEEE Geoscience and Remote Sensing Letters, 16(5), 786–790.
   Web of Science ®Google Scholar
• Isikdogan, F., Bovik, A. C., & Passalacqua, P. (2017). Surface water mapping by deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11), 4909–4918.
   Web of Science ®Google Scholar
• Jeppesen, J. H., Jacobsen, R. H., Inceoglu, F., & Toftegaard, T. S. (2019). A cloud detection algorithm for satellite imagery based on deep learning. Remote Sensing of Environment, 229, 247–259.
   Web of Science ®Google Scholar
• Ji, S., Wei, S., & Lu, M. (2018). Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Transactions on Geoscience and Remote Sensing, 57(1), 574–586.
   Web of Science ®Google Scholar
• Ji, S., Wei, S., & Lu, M. (2019). A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery. International Journal of Remote Sensing, 40(9), 3308–3322.
   Web of Science ®Google Scholar
• Jia, D., Song, C., Cheng, C., Shen, S., Ning, L., & Hui, C. (2020). A novel deep learning-based spatiotemporal fusion method for combining satellite images with different resolutions using a two-stream convolutional neural network. Remote Sensing, 12(4), 698.
   Web of Science ®Google Scholar
• Jiang, K., Wang, Z., Yi, P., Wang, G., Lu, T., & Jiang, J. (2019). Edge-enhanced GAN for remote sensing image superresolution. IEEE Transactions on Geoscience and Remote Sensing, 57(8), 5799–5812.
   Web of Science ®Google Scholar
• Khan, N., Chaudhuri, U., Banerjee, B., & Chaudhuri, S. (2019). Graph convolutional network for multi-label VHR remote sensing scene recognition. Neurocomputing, 357, 36–46.
   Web of Science ®Google Scholar
• Kipf, T. N., & Welling, M. (2016a). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
   Google Scholar
• Kipf, T. N., & Welling, M. (2016b). Variational graph auto-encoders. arXiv preprint arXiv:1611.07308.
   Google Scholar
• Lea, C., Vidal, R., Reiter, A., & Hager, G. D. (2016). Temporal convolutional networks: A unified approach to action segmentation. European Conference on Computer Vision. Springer, Cham, 2016: 47–54.
   Google Scholar
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
   PubMed Web of Science ®Google Scholar
• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
   Web of Science ®Google Scholar
• Lei, S., Shi, Z., & Zou, Z. (2017). Super-resolution for remote sensing images via local–global combined network. IEEE Geoscience and Remote Sensing Letters, 14(8), 1243–1247.
   Web of Science ®Google Scholar
• Li, J., Li, Y., He, L., Chen, J., & Plaza, A.  (2020a). Spatio-temporal fusion for remote sensing data: An overview and new benchmark. Information Sciences, 63(140301), 1–140301.
   Google Scholar
• Li, K., Cheng, G., Bu, S., & You, X. (2018). Rotation-insensitive and context-augmented object detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 56(4), 2337–2348.
   Web of Science ®Google Scholar
• Li, K., Wan, G., Cheng, G., Meng, L., & Han, J. (2020b). Object detection in optical remote sensing images: A survey and a new benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 296–307.
   Web of Science ®Google Scholar
• Li, L., Yan, Z., Shen, Q., Cheng, G., Gao, L., & Zhang, B. (2019a). Water body extraction from very high spatial resolution remote sensing data based on fully convolutional networks. Remote Sensing, 11(10), 1162.
   Web of Science ®Google Scholar
• Li, M., Zhang, Z., Lei, L., Wang, X., & Guo, X. (2020c). Agricultural greenhouses detection in high-resolution satellite images based on convolutional neural networks: Comparison of faster R-CNN, YOLO v3 and SSD. Sensors, 20(17), 4938.
   Web of Science ®Google Scholar
• Li, X., Lei, L., Sun, Y., Li, M., & Kuang, G. (2020d). Multimodal bilinear fusion network with second-order attention-based channel selection for land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1011–1026.
   Web of Science ®Google Scholar
• Li, X., Yao, X., & Fang, Y. (2018a). Building-a-nets: Robust building extraction from high-resolution remote sensing images with adversarial networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10), 3680–3687.
   Web of Science ®Google Scholar
• Li, Y., Chen, W., Zhang, Y., Tao, C., Xiao, R., & Tan, Y. (2020e). Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning. Remote Sensing of Environment, 250, 112045.
   Web of Science ®Google Scholar
• Li, Y., Peng, C., Chen, Y., Jiao, L., Zhou, L., & Shang, R. (2019b). A deep learning method for change detection in synthetic aperture radar images. IEEE Transactions on Geoscience and Remote Sensing, 57(8), 5751–5763.
   Web of Science ®Google Scholar
• Li, Y., Zhang, H., Xue, Y., Jiang, Y., & Shen, Q. (2018b). Deep learning for remote sensing image classification: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(6), e1264.
   Web of Science ®Google Scholar
• Li, Y., Zhang, Y., Huang, X., & Yuille, A. L. (2018c). Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 182–196.
   Web of Science ®Google Scholar
• Liang, J., Deng, Y., & Zeng, D. (2020). A deep neural network combined CNN and GCN for remote sensing scene classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4325–4338.
   Web of Science ®Google Scholar
• Liu, H., Luo, J., Huang, B., Hu, X., Sun, Y., Yang, Y., Xu, N., & Zhou, N. (2019a). DE-Net: Deep encoding network for building extraction from high-resolution remote sensing imagery. Remote Sensing, 11(20), 2380.
   Web of Science ®Google Scholar
• Liu, H., & Weng, Q. (2018a). Scaling effect of fused ASTER-MODIS land surface temperature in an urban environment. Sensors, 18(11), 4058.
   PubMed Web of Science ®Google Scholar
• Liu, J., Gong, M., Qin, K., & Zhang, P.  (2016a). A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE Transactions on Neural Networks and Learning Systems, 29(3), 545–559.
   PubMed Web of Science ®Google Scholar
• Liu, L., Wang, J., Zhang, E., Li, B., Zhu, X., Zhang, Y., & Peng, J. (2020a). Shallow–deep convolutional network and spectral-discrimination-based detail injection for multispectral imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1772–1783.
   Web of Science ®Google Scholar
• Liu, P. (2015). A survey of remote-sensing big data. Frontiers in Environmental Science, 3, 45.
   Web of Science ®Google Scholar
• Liu, P., Liu, X., Liu, M., Shi, Q., Yang, J., Xu, X., & Zhang, Y. (2019b). Building footprint extraction from high-resolution images via spatial residual inception convolutional neural network. Remote Sensing, 11(7), 830.
   Web of Science ®Google Scholar
• Liu, Q., Kampffmeyer, M., Jenssen, R., & Salberg, A.  (2020b). SCG-Net: Self-constructing graph neural networks for semantic segmentation. arXiv preprint arXiv:2009.01599.
   Google Scholar
• Liu, Q., Xu, L., & Zhang, Z. The downscaling of the SMOS global sea surface salinity product based on MODIS data using a deep convolution network approach. Proceedings of the 2019 3rd International Conference on Advances in Image Processing. 2019c: 97–100.Chengdu, China.
   Google Scholar
• Liu, R., Jiang, D., Zhang, L., & Zhang, Z. (2020c). Deep depthwise separable convolutional network for change detection in optical aerial images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1109–1118.
   Web of Science ®Google Scholar
• Liu, W., Wang, J., Luo, J., Wu, Z., Chen, J., Zhou, Y., … Yang, Y. (2020d). Farmland parcel mapping in mountain areas using time-series SAR data and VHR optical images. Remote Sensing, 12(22), 3733.
   Web of Science ®Google Scholar
• Liu, X., Deng, C., Chanussot, J., Hong, D., & Zhao, B. (2019d). StfNet: A two-stream convolutional neural network for spatiotemporal image fusion. IEEE Transactions on Geoscience and Remote Sensing, 57(9), 6552–6564.
   Web of Science ®Google Scholar
• Liu, Y., Chen, X., Wang, Z., Wang, Z. J., Ward, R. K., & Wang, X. (2018b). Deep learning for pixel-level image fusion: Recent advances and future prospects. Information Fusion, 42, 158–173.
   Web of Science ®Google Scholar
• Liu, Y., Gross, L., Li, Z., Li, X., Fan, X., & Qi, W. (2019e). Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling. IEEE Access, 7, 128774–128786.
   Web of Science ®Google Scholar
• Liu, Y., Yao, J., Lu, X., Xia, M., Wang, X., & Liu, Y. (2018c). Roadnet: Learning to comprehensively analyze road networks in complex urban scenes from high-resolution remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing, 57(4), 2043–2056.
   Web of Science ®Google Scholar
• Liu, Z., Wang, H., Weng, L., & Yang, Y. (2016b). Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds. IEEE Geoscience and Remote Sensing Letters, 13(8), 1074–1078.
   Web of Science ®Google Scholar
• Long, Y., Gong, Y., Xiao, Z., & Liu, Q. (2017). Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(5), 2486–2498.
   Web of Science ®Google Scholar
• López-Serrano, P. M., López-Sánchez, C. A., Álvarez-González, J. G., & García-Gutiérrez, J. (2016). A comparison of machine learning techniques applied to landsat-5 TM spectral data for biomass estimation. Canadian Journal of Remote Sensing, 42(6), 690–705.
   Web of Science ®Google Scholar
• Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
   Web of Science ®Google Scholar
• Lu, X., Zhong, Y., Zheng, Z., Liu, Y., Zhao, J., Ma, A., & Yang, J. (2019). Multi-scale and multi-task deep learning framework for automatic road extraction. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9362–9377.
   Web of Science ®Google Scholar
• Lv, X., Ming, D., Lu, T., Zhou, K., Wang, M., & Bao, H. (2018). A new method for region-based majority voting CNNs for very high resolution image classification. Remote Sensing, 10(12), 1946.
   Web of Science ®Google Scholar
• Lyu, H., Lu, H., & Mou, L. (2016). Learning a transferable change rule from a recurrent neural network for land cover change detection. Remote Sensing, 8(6), 506.
   Web of Science ®Google Scholar
• Ma, J., Yu, W., Chen, C., Liang, P., Guo, X., & Jiang, J. (2020). Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion. Information Fusion, 62, 110–120.
   Web of Science ®Google Scholar
• Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019a). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177.
   Web of Science ®Google Scholar
• Ma, W., Pan, Z., Guo, J., & Lei, B. (2019b). Achieving super-resolution remote sensing images via the wavelet transform combined with the recursive res-net. IEEE Transactions on Geoscience and Remote Sensing, 57(6), 3512–3527.
   Web of Science ®Google Scholar
• Ma, W., Zhang, J., Wu, Y., Jiao, L., Zhu, H., & Zhao, W. (2019c). A novel two-step registration method for remote sensing images based on deep and local features. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4834–4843.
   Web of Science ®Google Scholar
• Maltezos, E., Doulamis, N., Doulamis, A., & Ioannidis, C. (2017). Deep convolutional neural networks for building extraction from orthoimages and dense image matching point clouds. Journal of Applied Remote Sensing, 11(4), 042620.
   Google Scholar
• Marmanis, D., Schindler, K., Wegner, J. D., Galliani, S., Datcu, M., & Stilla, U. (2018). Classification with an edge: Improving semantic image segmentation with boundary detection. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 158–172.
   Web of Science ®Google Scholar
• Masi, G., Cozzolino, D., Verdoliva, L., & Scarpa, G. (2016). Pansharpening by convolutional neural networks. Remote Sensing, 8(7), 594.
   Web of Science ®Google Scholar
• Mateo-García, G., Laparra, V., López-Puigdollers, D., & Gómez-Chova, L. (2020). Transferring deep learning models for cloud detection between Landsat-8 and Proba-V. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 1–17.
   Web of Science ®Google Scholar
• Merkle, N., Auer, S., Müller, R., & Reinartz, P. (2018). Exploring the potential of conditional adversarial networks for optical and SAR image matching. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(6), 1811–1820.
   Web of Science ®Google Scholar
• Mou, L., Bruzzone, L., & Zhu, X. X. (2018). Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(2), 924–935.
   Web of Science ®Google Scholar
• Mou, L., Lu, X., Li, X., & Zhu, X. X. (2020). Nonlocal graph convolutional networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(12), 8246–8257.
   Web of Science ®Google Scholar
• Ogut, M., Bosch-Lluis, X., & Reising, S. C. (2019). Deep learning calibration of the high-frequency airborne microwave and millimeter-wave radiometer (HAMMR) instrument. IEEE Transactions on Geoscience and Remote Sensing, 58(5), 3391–3399.
   Web of Science ®Google Scholar
• Ouyang, S., & Li, Y. (2021). Combining deep semantic segmentation network and graph convolutional neural network for semantic segmentation of remote sensing imagery. Remote Sensing, 13(1), 119.
   Web of Science ®Google Scholar
• Pan, X., Zhao, J., & Xu, J. (2020). An end-to-end and localized post-processing method for correcting high-resolution remote sensing classification result images. Remote Sensing, 12(5), 852.
   Web of Science ®Google Scholar
• Peng, D., Zhang, Y., & Guan, H. (2019). End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sensing, 11(11), 1382.
   Web of Science ®Google Scholar
• Peng, H., Wang, H., Du, B., Bhuiyan, M. Z. A., Ma, H., Liu, J., & Yu, P. S. (2020). Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Information Sciences, 521, 277–290.
   Web of Science ®Google Scholar
• Peng, J., Wang, D., Liao, X., Shao, Q., Sun, Z., Yue, H., & Ye, H. (2020). Wild animal survey using UAS imagery and deep learning: Modified Faster R-CNN for kiang detection in Tibetan Plateau. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 364–376.
   Web of Science ®Google Scholar
• Qin, A., Shang, Z., Tian, J., Wang, Y., Zhang, T., & Tang, Y. Y. (2018). Spectral–spatial graph convolutional networks for semisupervised hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 16(2), 241–245.
   Web of Science ®Google Scholar
• Qiu, S., Zhu, Z., & He, B. (2019). Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery. Remote Sensing of Environment, 231, 111205.
   Web of Science ®Google Scholar
• Rahman, M. S., & Di, L. (2017). The state of the art of spaceborne remote sensing in flood management. Natural Hazards, 85(2), 1223–1248.
   Web of Science ®Google Scholar
• Ren, Y., Zhu, C., & Xiao, S. (2018). Small object detection in optical remote sensing images via modified faster R-CNN. Applied Sciences, 8(5), 813.
   Google Scholar
• Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. 2011 International conference on computer vision. IEEE, 2011: 2564–2571.
   Google Scholar
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation[R]. California Univ San Diego La Jolla Inst for Cognitive Science.La Jolla, CA, USA.
   Google Scholar
• Saha, S., Mou, L., Zhu, X., & Bovolo, F. (2020). Semisupervised change detection using graph convolutional network. IEEE Geoscience and Remote Sensing Letters. doi:10.1109/LGRS.2020.2985340
   Google Scholar
• Scarpa, G., Gargiulo, M., Mazza, A., & Gaetano, R. (2018). A cnn-based fusion method for feature extraction from sentinel data. Remote Sensing, 10(2), 236.
   Web of Science ®Google Scholar
• Seo, D. K., Kim, Y. H., Eo, Y. D., Park, W., & Park, H. (2017). Generation of radiometric, phenological normalized image based on random forest regression for change detection. Remote Sensing, 9(11), 1163.
   Web of Science ®Google Scholar
• Shao, Z., Cai, J., Fu, P., Hu, L., & Liu, T. (2019). Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product. Remote Sensing of Environment, 235, 111425.
   Web of Science ®Google Scholar
• Shao, Z., Deng, J., Wang, L., Fan, Y., Sumari, N., & Cheng, Q. (2017). Fuzzy autoencode based cloud detection for remote sensing imagery. Remote Sensing, 9(4), 311.
   Web of Science ®Google Scholar
• Sharma, A., Liu, X., Yang, X., & Shi, D. (2017). A patch-based convolutional neural network for remote sensing image classification. Neural Networks, 95, 19–28.
   PubMed Web of Science ®Google Scholar
• Shen, H., Zhou, M., Li, T., & Zeng, C. (2019). Integration of remote sensing and social sensing data in a deep learning framework for Hourly Urban PM2. 5 mapping. International Journal of Environmental Research and Public Health, 16(21), 4102.
   Web of Science ®Google Scholar
• Shi, M., Xie, F., Zi, Y., & Yin, J. (2016). Cloud detection of remote sensing images by deep learning. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2016: 701–704.
   Google Scholar
• Shi, X., Chen, Z., Wang, H., & Yeung D. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems, 28, 802–810.
   Google Scholar
• Shi, Y., Li, Q., & Zhu, X. X. (2020). Building segmentation through a gated graph convolutional neural network with deep structured feature embedding. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 184–197.
   PubMed Web of Science ®Google Scholar
• Song, H., Liu, Q., Wang, G., Hang, R., & Huang, B. (2018). Spatiotemporal satellite image fusion using deep convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3), 821–829.
   Web of Science ®Google Scholar
• Song, S., Liu, J., Liu, Y., Feng, G., Han, H., Yao, Y., & Du, M. (2020). Intelligent object recognition of urban water bodies based on deep learning for multi-source and multi-temporal high spatial resolution remote sensing imagery. Sensors, 20(2), 397.
   Web of Science ®Google Scholar
• Sun, L., Mi, X., Wei, J., Wang, J., Tian, X., Yu, H., & Gan, P. (2017). A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 70–88.
   Web of Science ®Google Scholar
• Sun, Z., Di, L., & Fang, H. (2019). Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series. International Journal of Remote Sensing, 40(2), 593–614.
   Web of Science ®Google Scholar
• Tan, Z., Yue, P., Di, L., & Tang, J. (2018). Deriving high spatiotemporal remote sensing images using deep convolutional network. Remote Sensing, 10(7), 1066.
   Web of Science ®Google Scholar
• Teimouri, N., Dyrmann, M., & Jørgensen, R. N. (2019). A novel spatio-temporal FCN-LSTM network for recognizing various crop types using multi-temporal radar images. Remote Sensing, 11(8), 990.
   Web of Science ®Google Scholar
• Timilsina, S., Aryal, J., & Kirkpatrick, J. B. (2020). Mapping urban tree cover changes using object-based convolution neural network (OB-CNN). Remote Sensing, 12(18), 3017.
   Web of Science ®Google Scholar
• Tong, X., Xia, G., Lu, Q., Shen, H.,  Li, S., You, S., & Zhang, L. (2018). Learning transferable deep models for land-use classification with high-resolution remote sensing images. arXiv preprint arXiv:1807.05713.
   Google Scholar
• Tong, X. Y., Xia, G. S., Lu, Q., Shen, H., Li, S., You, S., & Zhang, L. (2020). Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237, 111322.
   Web of Science ®Google Scholar
• Tuna, C., Unal, G., & Sertel, E. (2018). Single-frame super resolution of remote-sensing images by convolutional neural networks. International Journal of Remote Sensing, 39(8), 2463–2479.
   Web of Science ®Google Scholar
• Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., &Bengio, Y.  (2017). Graph attention networks. arXiv preprint arXiv:1710.10903.
   Google Scholar
• Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018, 1–13.
   Web of Science ®Google Scholar
• Waldner, F., & Diakogiannis, F. I. (2020). Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network. Remote Sensing of Environment, 245, 111741.
   Web of Science ®Google Scholar
• Wan, S., Gong, C., Zhong, P., Du, B., Zhang L., & Yang, J. (2019). Multiscale dynamic graph convolutional network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(5), 3162–3177.
   Web of Science ®Google Scholar
• Wan, S., Gong, C., Zhong, P., Du, B., Zhang, L., & Yang, J. (2020). Multiscale dynamic graph convolutional network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 58(5), 3162–3177.
   Web of Science ®Google Scholar
• Wang, C., Bai, X., Wang, S., Zhou, J., & Ren, P. (2018a). Multiscale visual attention networks for object detection in VHR remote sensing images. IEEE Geoscience and Remote Sensing Letters, 16(2), 310–314.
   Web of Science ®Google Scholar
• Wang, J., Zhang, J., Guo, Q., & Li, T. (2019). Fusion of hyperspectral and lidar data based on dual-branch convolutional neural network. IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019a: 3388–3391.
   Google Scholar
• Wang, Q., Yuan, Z., Du, Q., & Li, X.  (2018b). GETNET: A general end-to-end 2-D CNN framework for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 57(1), 3–13.
   Web of Science ®Google Scholar
• Wang, Q., Zhang, X., Chen, G., Dai, F., Gong, Y., & Zhu, K. (2018c). Change detection based on faster R-CNN for high-resolution remote sensing images. Remote Sensing Letters, 9(10), 923–932.
   Web of Science ®Google Scholar
• Wang, S., Quan, D., Liang, X., Ning, M., Guo, Y., & Jiao, L. (2018d). A deep learning framework for remote sensing image registration. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 148–164.
   Web of Science ®Google Scholar
• Wang, T., Li, Y., Yu, S., & Liu, Y. (2019b). Estimating the volume of oil tanks based on high-resolution remote sensing images. Remote Sensing, 11(7), 793.
   Web of Science ®Google Scholar
• Wang, W., Li, H., Deng, Y., Shao, L., Lu, X., & Du, Q. (2020). Generative adversarial capsule network with ConvLSTM for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters.
   Google Scholar
• Wang, W., Ma, L., Chen, M., & Du, Q. (2021). Joint correlation alignment-based graph neural network for domain adaptation of multitemporal hyperspectral remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3170–3184.
   Web of Science ®Google Scholar
• Wang, W., & Shen, J. (2017). Deep visual attention prediction. IEEE Transactions on Image Processing, 27(5), 2368–2378.
   Web of Science ®Google Scholar
• Wei, Y., Yuan, Q., Shen, H., & Zhang, L. (2017). Boosting the accuracy of multispectral image pansharpening by learning a deep residual network. IEEE Geoscience and Remote Sensing Letters, 14(10), 1795–1799.
   Web of Science ®Google Scholar
• Wen, C., Li, X., Yao, X., Peng, L., & Chi, T. (2021). Airborne LiDAR point cloud classification with global-local graph attention convolution neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 181–194.
   Web of Science ®Google Scholar
• Widyaningrum, E., Bai, Q., Fajari, M. K., & Lindenbergh, R. C. (2021). Airborne laser scanning point cloud classification using the DGCNN deep learning method. Remote Sensing, 13(5), 859.
   Web of Science ®Google Scholar
• Wu, W., Gao, X., Fan, J., Xia, L., Luo, J., & Zhou, Y. (2020a). Improved mask R-CNN-based cloud masking method for remote sensing images. International Journal of Remote Sensing, 41(23), 8910–8933.
   Web of Science ®Google Scholar
• Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P.S. (2020b). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32(1): 4–24.
   Google Scholar
• Xia, G., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo J., Datcu, M., Pelillo, M., & Zhang, L. (2018). DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3974–3983.
   Google Scholar
• Xie, F., Shi, M., Shi, Z., Yin, J., & Zhao, D. (2017). Multilevel cloud detection in remote sensing images based on deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(8), 3631–3640.
   Web of Science ®Google Scholar
• Xing, Y., Wang, M., Yang, S., & Jiao, L. (2018). Pan-sharpening via deep metric learning. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 165–183.
   Web of Science ®Google Scholar
• Xu, F., Cervone, G., Franch, G., & Salvador, M. (2020). Multiple geometry atmospheric correction for image spectroscopy using deep learning. Journal of Applied Remote Sensing, 14(2), 024518.
   Web of Science ®Google Scholar
• Xu, Q., Hou, Z., & Tokola, T. (2012). Relative radiometric correction of multi-temporal ALOS AVNIR-2 data for the estimation of forest attributes. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 69–78.
   Web of Science ®Google Scholar
• Yan, J., Chen, X., Chen, Y., & Liang, D. (2020a). Multistep prediction of land cover from dense time series remote sensing images with temporal convolutional networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5149–5161.
   Web of Science ®Google Scholar
• Yan, J., Mu, L., Wang, L., Ranjan, R., & Zomaya, A. Y. (2020b). temporal convolutional networks for the advance prediction of ENSO. Scientific Reports, 10(1), 1–15.
   PubMed Web of Science ®Google Scholar
• Yan, J., Wang, H., Yan, M., Diao, W., Sun, X., & Li, H. (2019). IoU-adaptive deformable R-CNN: Make full use of IoU for multi-class object detection in remote sensing imagery. Remote Sensing, 11(3), 286.
   Web of Science ®Google Scholar
• Yang, H., Wu, P., Yao, X., Wu, Y., Wang, B., & Xu, Y. (2018b). Building extraction in very high resolution imagery by dense-attention networks. Remote Sensing, 10(11), 1768.
   Web of Science ®Google Scholar
• Yang, H., Yu, B., Luo, J., & Chen, F. (2019). Semantic segmentation of high spatial resolution images with deep neural networks. GIScience & Remote Sensing, 56(5), 749–768.
   Web of Science ®Google Scholar
• Yang, H. L., Yuan, J., Lunga, D., Laverdiere, M., Rose, A., & Bhaduri, B. (2018a). Building extraction at scale using convolutional neural network: Mapping of the united states. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8), 2600–2614.
   Web of Science ®Google Scholar
• Yang, J., Fu, X., Hu, Y., Huang, Y., Ding, X., & Paisley, J. (2017). PanNet: A deep network architecture for pan-sharpening. Proceedings of the IEEE International Conference on Computer Vision. 2017: 5449–5457.
   Google Scholar
• Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., … Dickinson, R. (2013). The role of satellite remote sensing in climate change studies. Nature Climate Change, 3(10), 875–883.
   Web of Science ®Google Scholar
• Yang, Z., Dan, T., & Yang, Y. (2018c). Multi-temporal remote sensing image registration using deep convolutional features. IEEE Access, 6, 38544–38555.
   Web of Science ®Google Scholar
• Yao, H., Wu, F., Ke, J., Tang, X., Jia, Y., Lu, S., Gong, P., Ye, J., & Li, Z. (2018). Deep multi-view spatial-temporal network for taxi demand prediction. arXiv preprint arXiv:1802.08714.
   Google Scholar
• Ye, F., Su, Y., Xiao, H., Zhao, X., & Min, W. (2018). Remote sensing image registration using convolutional neural network features. IEEE Geoscience and Remote Sensing Letters, 15(2), 232–236.
   Web of Science ®Google Scholar
• Yin, Z., Ling, F., Foody, G. M., Li, X., & Du, Y. (2020). Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep convolutional neural network. Remote Sensing Letters, 11(12), 1181–1190.
   Web of Science ®Google Scholar
• Yin, Z., Wu, P., Foody, G. M., Wu, Y., Liu, Z., Du, Y., & Ling, F. (2020). Spatiotemporal fusion of land surface temperature based on a convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 59(2), 1808–1822.
   Web of Science ®Google Scholar
• Yuan, J. (2016). Automatic building extraction in aerial scenes using convolutional networks. arXiv preprint arXiv:1602.06564.
   Google Scholar
• Yuan, K., Meng, G., Cheng, D., Bai, J., Xiang, S., & Pan, C. (2017, September). Efficient cloud detection in remote sensing images using edge-aware segmentation network and easy-to-hard training strategy. In 2017 IEEE International Conference on Image Processing (ICIP) (pp. 61–65). IEEE. Beijing, China.
   Google Scholar
• Zhan, Y., Wang, J., Shi, J., Cheng, G., Yao, L., & Sun, W. (2017). Distinguishing cloud and snow in satellite images via deep convolutional network. IEEE Geoscience and Remote Sensing Letters, 14(10), 1785–1789.
   Web of Science ®Google Scholar
• Zhang, C., Harrison, P. A., Pan, X., Li, H., Sargent, I., & Atkinson, P. M. (2020a). Scale Sequence Joint Deep Learning (SS-JDL) for land use and land cover classification. Remote Sensing of Environment, 237, 111593.
   Web of Science ®Google Scholar
• Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., & Atkinson, P. M. (2019a). Joint deep learning for land cover and land use classification. Remote Sensing of Environment, 221, 173–187.
   Web of Science ®Google Scholar
• Zhang, C., Wei, S., Ji, S., & Lu, M. (2019b). Detecting large-scale urban land cover changes from very high resolution remote sensing images using cnn-based classification. ISPRS International Journal of Geo-Information, 8(4), 189.
   Web of Science ®Google Scholar
• Zhang, G., Lu, H., Dong, J., Poslad, S., Li, R., Zhang, X., & Rui, X. (2020b). A framework to predict high-resolution spatiotemporal PM2. 5 distributions using a deep-learning model: A case study of Shijiazhuang, China. Remote Sensing, 12(17), 2825.
   Web of Science ®Google Scholar
• Zhang, H., & Deng, Q. (2019c). Deep learning based fossil-fuel power plant monitoring in high resolution remote sensing images: A comparative study. Remote Sensing, 11(9), 1117.
   Web of Science ®Google Scholar
• Zhang, H., Ni, W., Yan, W., Xiang, D., Wu, J., Yang, X., & Bian, H. (2019d). Registration of multimodal remote sensing image based on deep fully convolutional neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(8), 3028–3042.
   Web of Science ®Google Scholar
• Zhang, L., Shi, Z., & Wu, J. (2015). A hierarchical oil tank detector with deep surrounding features for high-resolution optical satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4895–4909.
   Web of Science ®Google Scholar
• Zhang, L., Zhang, L., & Du, B. (2016a). Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2), 22–40.
   Web of Science ®Google Scholar
• Zhang, M., Xu, G., Chen, K., Yan, M., & Sun, X. (2018a). Triplet-based semantic relation learning for aerial remote sensing image change detection. IEEE Geoscience and Remote Sensing Letters, 16(2), 266–270.
   Web of Science ®Google Scholar
• Zhang, P., Gong, M., Su, L., Liu, J., & Li, Z. (2016b). Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 24–41.
   Web of Science ®Google Scholar
• Zhang, T., & Huang, X. (2018b). Monitoring of urban impervious surfaces using time series of high-resolution remote sensing images in rapidly urbanized areas: A case study of Shenzhen. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8), 2692–2708.
   Web of Science ®Google Scholar
• Zhang, Y., Yuan, Y., Feng, Y., & Lu, X. (2019e). Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection. IEEE Transactions on Geoscience and Remote Sensing, 57(8), 5535–5548.
   Web of Science ®Google Scholar
• Zhang, Z., Guo, W., Zhu, S., & Yu, W. (2018c). Toward arbitrary-oriented ship detection with rotated region proposal and discrimination networks. IEEE Geoscience and Remote Sensing Letters, 15(11), 1745–1749.
   Web of Science ®Google Scholar
• Zhang, Z., Liu, Q., & Wang, Y. (2018d). Road extraction by deep residual u-net. IEEE Geoscience and Remote Sensing Letters, 15(5), 749–753.
   Web of Science ®Google Scholar
• Zhang, Z., & Wang, Y. (2019f). JointNet: A common neural network for road and building extraction. Remote Sensing, 11(6), 696.
   Web of Science ®Google Scholar
• Zhao, H., Chen, Z., Jiang, H., Jing, W., Sun, L., & Feng, M. (2019). Evaluation of three deep learning models for early crop classification using Sentinel-1A imagery time series—A case study in Zhanjiang, China. Remote Sensing, 11(22), 2673.
   Web of Science ®Google Scholar
• Zhao, W., Wang, Z., Gong, M., & Liu, J. (2017). Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network. IEEE Transactions on Geoscience and Remote Sensing, 55(12), 7066–7080
   Web of Science ®Google Scholar
• Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., & Sun, M. (2018). Graph Neural Networks: A Review of Methods and Applications. arXiv preprint arXiv:1812 08434.
   Google Scholar
• Zhou, L., Zhang, C., & Wu, M. D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. CVPR Workshops. 2018b: 182–186.Salt Lake City, UT, USA.
   Google Scholar
• Zhou, P., Cheng, G., Liu, Z., Bu, S., & Hu, X. (2016). Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping. Multidimensional Systems and Signal Processing, 27(4), 925–944.
   Web of Science ®Google Scholar
• Zhou, Y., & Grassotti, C. (2020a). Development of a machine learning-based radiometric bias correction for NOAA’s microwave integrated retrieval system (MiRS). Remote Sensing, 12(19), 3160.
   Web of Science ®Google Scholar
• Zhou, Y., Li, J., Feng, L., Zhang, X., & Hu, X. (2017). Adaptive scale selection for multiscale segmentation of satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(8), 3641–3651.
   Web of Science ®Google Scholar
• Zhou, Y., Luo, J., Feng, L., Yang, Y., Chen, Y., & Wu, W. (2019). Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data. GIScience & Remote Sensing, 56(8), 1170–1191.
   Web of Science ®Google Scholar
• Zhou, Y., Luo, J., Shen, Z., Hu, X., & Yang, H. (2014). Multiscale water body extraction in urban environments from satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4301–4312.
   Web of Science ®Google Scholar
• Zhou, Y. N., Yang, X., Feng, L., Wu, W., Wu, T., Luo, J., & Zhang, X. (2020). Superpixel-based time-series reconstruction for optical images incorporating SAR data using autoencoder networks. GIScience & Remote Sensing, 57(8), 1005–1025
   Google Scholar
• Zhu, H., Jiao, L., Ma, W., Liu, F., & Zhao, W. (2019). A novel neural network for remote sensing image matching. IEEE Transactions on Neural Networks and Learning Systems, 30(9), 2853–2865.
   PubMed Web of Science ®Google Scholar
• Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8–36.
   Web of Science ®Google Scholar