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Zhao, J., Zhong, Y., Wei, L., Zhang, L., 2020, A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions, Remote Sensing of Environment,
Tong, X., Xia, G., 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, Vol.237, 111322.
Xue, N., Bai, S., Wang, F., Xia, G., Zhang, L., 2019, Learning Regional Attraction for Line Segment Detection, IEEE Trans. on Pattern Analysis and Machine Intelligence，DOI: 10.1109/TPAMI.2019.2958642.
Shi, L., Li, P., Yang, L., Zhang, L., Ding, L., Zhao, L., 2020, Polarimetric SAR Calibration and Residual Error Estimation When Corner Reflectors are Unavailable, IEEE Trans. on Geoscience and Remote Sensing,
Wan, Y., Zhong, Y., Ma, A., Zhang, L., 2019, Multi-Objective Hyperspectral Feature Selection Based on Discrete Sine Cosine Algorithm, IEEE Trans. on Geoscience and Remote Sensing,
Chen, H., Wu, C., Du, B., Zhang, L., 2019, Change Detection in Multi-source VHR Images via Deep Siamese Convolutional Multiple-layers Recurrent Neural Network, IEEE Trans. on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2019.2956756.
Chang, S., Du, B., Zhang, L., 2019, A Subspace Selection Based Discriminative Forest Method for Hyperspectral Anomaly Detection, IEEE Trans. on Geoscience and Remote Sensing,
Zhong, Y., Li, W., Wang, X., Jin, S., Zhang, L., 2020, Satellite-ground integrated destriping network: A new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets, Remote Sensing of Environment, Vol. 237, 111416.
Li, X., Feng, R., Guan, X., Shen, H., Zhang, L., 2019, Remote Sensing Image Mosaicking: Achievements and Challenges, IEEE Trans. on Geoscience and Remote Sensing, Vol.4, No.4, PP.8-12.
Tong, X., Xia, G., Zhong, Y., Zhang, L., 2019, Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation, IEEE Trans．on Big Data, DOI: 10.1109/TBDATA.2019.2948924.
Yuan, Q., Xu, H., Shen, H., Zhang, L., 2020, Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements, Journal of Hydrology, Vol. 580, 124351.
Luo, F., Zhang, L., Du, B., Zhang, L., 2019, Dimensionality Reduction with Enhanced Hybrid-graph Discriminant Learning for Hyperspectral Image Classification, IEEE Trans. on Geoscience and Remote Sensing,
Wan, Y., Zhong, Y., Ma, A., Zhang, L., 2019, Multi-Objective Sparse Subspace Clustering for Hyperspectral Imagery, IEEE Trans. on Geoscience and Remote Sensing,
Zhang, H., Liu, L., He, D., Zhang, H., 2019, Hyperspectral Image Denoising with Total Variation Regularized Non-local Low-Rank Tensor Decomposition, IEEE Trans. on Geoscience and Remote Sensing,
He, D., Zhong, Y., Zhang, L., 2019, Spectral-spatial-temporal MAP based sub-pixel mapping for land cover change detection, IEEE Trans. on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2019.2947708.
Xu, Y., Du, B., Zhang, L., 2019, Beyond the Patchwise Classification: Spectral-Spatial Fully Convolutional Networks for Hyperpsectral Image Classificaiton, IEEE Trans. on Big Data,
Du, B., Ru, L., Wu, C., Zhang, L., 2019, Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images, IEEE Trans. on Geoscience and Remote Sensing, Vol. 57, No.12, PP. 9976-9992.
Xu, Y., Du, B., Zhang, L., 2019, Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.12, No.6, PP.1709-1724.
Xiang, T., Xia, G., Zhang, L., 2019, Mini-Unmanned Aerial Vehicle-Based Remote Sensing, IEEE Geoscience and Remote Sensing Magazine, Vol.7, No.3, PP.29-63.
Li, Z., Shen, H., Cheng, Q., Li, W., Zhang, L.; Thick Cloud Removal in High-Resolution Satellite Images Using Stepwise Radiometric Adjustment and Residual Correction, Remote Sensing，Vol.11, No.16, 1925, DOI: 10.3390/rs11161925.
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