张良培|个人主页 Prof. Liangpei Zhang|Homepage

文章 Publications (Appeared in international journal) 英文部分 ( 论文与学生合作完成,本人为通讯作者 )

• Xue, N., Xia, G., Bai, X., Zhang, L., 2017, Anisotropic-Scale Junction Detection and Matching for Indoor Images, IEEE Trans. on Image Processing, DOI: 10.1109/TIP.2017.2754945.

• 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, Vol. 14, No. 10, PP.1795-1799.

• Ma, A., Zhong, Y., Zhang, L., Multi-Objective Subpixel Land-Cover Mapping, IEEE Trans. on Geoscience and Remote Sensing,

• Luo, R., Liao, W., Zhang, H., Zhang, L., 2017, Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote Sensed Scene, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.10, No.8, PP. 3768-3781.

• Tao, Y., Xu, M., Zhang, F., Du, B., Zhang, L., 2017, Unsupervised-restricted deconvolutional neural network for very high resolution remote sensing image classification, IEEE Trans. on Geoscience and Remote Sensing,

• Wu, C., Du, B., Cui, X., Zhang, L., 2017, A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion, Remote Sensing of Environment, Vol.199, PP.241-255,

• Wang, X., Zhong, Y., Zhang, L, Xu, Y., 2017, Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing, IEEE Trans. on Geoscience and Remote Sensing,

• Xia, G., Liu, G., Bai, X., Zhang, L., 2017, Exture characterization by using shape co-occurrence patterns, IEEE Trans. on Image Processing, Vol. 26, No. 10, PP.5005-5018.

• Chang, S, Du, B., Zhang, L., Zhao, R., 2017, IBRS: An Iterative Background Reconstruction and Suppression Framework for Hyperspectral Target Detection, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.10, No.7, PP.3406-3417.

• Han, X., Zhong, Y., Zhang, L., 2017, An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery, Remote Sens. 2017, 9, 666; doi:10.3390/rs9070666,

• Jiang, C., Doute, S., Luo, B., Zhang, L., 2017, Fusion of photogrammetric and photoclinometric information for highresolution DEMs from Mars in-orbit imagery, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.130, PP.418–430.

• Li, X., Fu, W., Shen, H., Huang, C., Zhang, L., 2017, Monitoring snow cover variability (2000–2014) in the Hengduan Mountains based on cloud-removed MODIS products with an adaptive spatio-temporal weighted method, Journal of Hydrology, Vol. 551, PP.314-327,

• Zhu, Q., Zhong, Y., Zhang, L., Li, D., 2017, Scene Classification Based on Fully Sparse Semantic Topic Model, IEEE Trans. on Geoscience and Remote Sensing, Vol. 55, No. 10, PP.5525-5538.

• Cheng, Q., Shen, H., Zhang, L., 2017, Missing Information Reconstruction for Single Remote Sensing Images Using Structure-Preserving Global Optimization, IEEE Signal Processing Letters, Vol.24, No.8, PP.1163-1167.

• Cheng, Q., Shen, H., Liu, H., Zhang, L., 2017, A Spatial and Temporal Non-Local Filter Based Data Fusion Method, IEEE Trans. on Geoscience and Remote Sensing, Vol.55, No.8, PP.4476-4488,

• Sun, W., Zhang, L., 2017, A Sparse and Low-Rank Near-Isometric Linear Embedding Method for Feature Extraction in Hyperspectral Imagery Classification, IEEE Trans. on Geoscience and Remote Sensing, Vol. 55, No. 7,PP.4032-4006,

• He, W., Zhang, H., Zhang, L., 2017, Total Variation Regularized Reweighted Sparse Non-Negative Matrix Factorization for Hyperspectral Unmixing, IEEE Trans. on Geoscience and Remote Sensing, Vol. 55, No. 7,PP.3909-3921.,

• Xia, G., Hu, J., Fu, F., Bai, X., Zhong, Y., Zhang, L., 2017, AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification, IEEE Trans. on Geoscience and Remote Sensing, Vol. 55, No. 7,PP.3965-3981.

• Jiang, H., Shen, H., Li, H., Lei, F., Gan, W., Zhang, L., 2017, Evaluation of Multiple Downscaled Microwave Soil Moisture Products over the Central Tibetan Plateau, Remote Sens., 9, 402; doi:10.3390/rs9050402.

• Tang, Y., Zhang, L., 2017, Urban Change Analysis with Multi-Sensor Multispectral Imagery, Remote Sens., 9, 252; doi:10.3390/rs9030252,

• Xu,M., Zhang, L., Du, B., and Zhang, L., 2017, A Mutation Operator Accelerated Quantum-Behaved Particle Swarm Optimization Algorithm for Hyperspectral Endmember Extraction, Remote Sensing, 9(3), 197, doi:10.3390/rs9030197.

• Liu, R., Zhang, L., Du, B., 2017, A Novel Endmember Extraction Method for Hyperspectral Imagery Based on Quantum-Behaved Particle Swarm Optimization, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.10, No.4, PP.1610-1631.

• Han, X., Zhong, Y., Zhang, L., 2017, Spatial-Spectral Unsupervised Convolutional Sparse Auto-Encoder Classifier for Hyperspectral Imagery, PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, Vol.83, No.3, PP:195-206.

• Sun, W., Ma, J., Yang, G., Du, B., Zhang, L., 2017, A Poisson nonnegative matrix factorization method with parameter subspace clustering constraint for endmember extraction in hyperspectral imagery, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.128, PP.27–39.

• Wang, Z., Du, B., Zhang, L., 2017, A Novel Semi-Supervised Active Learning Algorithm for Hyperspectral Image Classification, IEEE Trans. on Geoscience and Remote Sensing, Vol.55, No.6, PP.3071-3083.

• Zhao, R., Du, B., Zhang, L., 2017, Hyperspectral Anomaly Detection via A Sparsity Score Estimation Framework, IEEE Trans. on Geoscience and Remote Sensing, Vol.55, No.6, PP.3208-3222.

• Li, Z., Shen, H., Li, H., Xia, G., Gamba, P., Zhang, L., 2017, Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery, Remote Sensing of Environment, 191, 342-358.

• Du, B., Wang, Z., Zhang, L., Zhang, L., Tao, D., 2017, Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion, IEEE Trans. on Image Processing, Vol.26, No.4,Pp.1694-1707.

• Li, X., Zhang, L., Du, B., Shi, Q., 2017, An Iterative Reweighting Heterogeneous Transfer Learning Framework for Supervised Remote Sensing Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.10, No.5, PP.2022-2035.

• Dong, Y., Du, B., Zhang, L., Zhang, L., 2017, Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning, IEEE Trans. on Geoscience and Remote Sensing, Vol.55, No.5, PP.2509-2524.

• Zhai, H., Zhang, H., Zhang, L., Li, P., 2017, Plaza, A., 2017, A New Sparse Subspace Clustering Algorithm for Hyperspectral Remote Sensing Imagery, IEEE Geoscience and Remote Sensing Letters, Vol.14, PP.43-47.

• Li, T., Shen, H., Zeng, C., Yuan, Q., Zhang, L., 2017, Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment, Atmospheric Environment,Vol.152, PP.477–489.

• Du, B., Wang, Z., Zhang, L, Zhang, L., Liu, W, Shen, J., Tao, D., 2017, Exploring Representativeness and Informativeness for Active Learning, IEEE Trans. on Cybernetics, Vol:47, PP.14-26.

• Wu, C., Zhang, L., Du, B., Kernel Slow Feature Analysis for Scene Change Detection, IEEE Trans. on Geoscience and Remote Sensing, Vol.55, No.4, PP.2367-2384.

• Yue, L., Shen, H., Zhang, L., 2017, High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations, ISPRS Journal of Photogrammetry and Remote Sensing,123,PP.20–34.

• Ma, X., Shen, H., Zhang, L., 2016,PolSAR anisotropic diffusion filter with a refined similarity measure and an adaptive fidelity constraint, International Journal of Remote Sensing, vo.37, no.24, PP. 5988-6011.

• Zhao, B., Zhong, Y., Ma, A., Zhang, L., 2016, A Spatial Gaussian Mixture Model for Optical Remote Sensing Image Clustering, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.9, No.12, PP.5748-5759.

• Feng, R., Zhong, Y., Zhang, L., 2016, Adaptive Spatial Regularization Sparse Unmixing Strategy Based on Joint MAP for Hyperspectral Remote Sensing Imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.9, No.12, PP.5071-5805.

• Lv, P., Zhong, Y., Zhao, J., Jiao, H., Zhang, L., 2016, Change Detection Based on a Multifeature Probabilistic Ensemble Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery, IEEE Geoscience and Remote Sensing Letters, Vol.13, No.12, PP.1965-1969.

• Zhao, R., Du, B., Zhang, L., Zhang, L., 2016, A robust background regression based score estimation algorithm for hyperspectral anomaly detection, ISPRS Journal of Photogrammetry and Remote Sensing, 122, PP.126–144.

• Zhang, Y., Du, B., Zhang, L., 2017, Joint Sparse Representation and Multi-Task Learning for Hyperspectral Target Detection, IEEE Trans. on Geoscience and Remote Sensing, Vol.20, No.2, PP.894-906.

• Peng, X., Shen, H, Zhang, L., Zeng, C., Yang, G., and He, Z, 2016, Spatially Continuous Mapping of Daily Global Ozone Distribution (2004-2014) with the Aura OMI Sensor: Spatially Continuous Ozone Product, Journal of Geophysical Research, Atmospheres, Vol.121, No.21, PP.12702-12722.

• Zhong, Y., Fei, F., Liu, Y., Zhao, B., Jiao, H., Zhang, L, 2016, SatCNN: satellite image dataset classification using agile convolutional neural networks, Remote Sensing Letters, Doi/full/10.1080/2150704X.2016.1235299.

• Zhao, R., Zhang, L., 2017, GSEAD: Graphical Scoring Estimation for Hyperspectral Anomaly Detection, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.10, No.2, PP.725- 739.

• Zhang, Y., Du, B., Zhang, L., Tao, D., 2016, Beyond the Sparsity-Based Target Detector: A Hybrid Sparsity and Statistics Based Detector for Hyperspectral Images, IEEE Trans. on Image Processing,Vol. 25, No. 11, PP.5345-5357.

• Shen, H., Meng, X., Zhang, L., 2016, An Integrated Framework for the Spatio-temporal-spectral Fusion of Remote Sensing Images, IEEE Trans. on Geoscience and Remote Sensing,Vol.54, No.12, PP.7135-7148.

• Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., Zhang, L,2016,Image super-resolution: The techniques, applications, and future, Signal Processing, Vol.128, PP.389-408.

• Meng, X., Li, J., Shen, H., Zhang, L., Zhang, H., 2016, Pansharpening with a Guided Filter Based on Three-Layer Decomposition, Sensors, Vol. 16, No. 7, PP.1068.

• Liu, R., Du, B., Zhang, L., 2016, Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF, REMOTE SENSING, Vol. 8, No. 6, PP.464.

• He, W., Zhang, H., Zhang, L., 2016, Sparsity-Regularized Robust Non-Negative Matrix Factorization for Hyperspectral Unmixing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.9, No.9, PP.4267-4279.

• Sun, W, Zhang, L., Zhang, L, Lai, Y., 2016, A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.9, No.9, PP.4374-4388.

• Li, X., Shen, H., Li, H., Zhang, L., 2016, Patch Matching-Based Multitemporal Group Sparse Representation for the Missing Information Reconstruction of Remote-Sensing Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, No.8, PP.3629-3641.

• Dong, Y., Du, B., Zhang, L., Zhang, L., 2017, Exploring Locally Adaptive Dimensionality Reduction for Hyperspectral Image Classification: A Maximum Margin Metric Learning Aspect, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.10, No.3, PP.1136-1150.

• Zhang, L., Zhu, X., Zhang, L., Du, B., 2016, Multi-domain Subspace Classification for Hyperspectral Images, IEEE Trans. on Geoscience and Remote Sensing,Vol. 54, No. 10,PP.6138-6150.

• Li, J., Yuan, Q., Shen, H., X, Meng, and Zhang, L., 2016, Hyperspectral Image Super-Resolution by Spectral Mixture Analysis and Spatial-Spectral Group Sparsity, IEEE Geoscience and Remote Sensing Letters, Vol. 13, No. 9, PP.1250-1254.

• Wu, C., Zhang, L., Zhang, L., 2016, A scene change detection framework for multi-temporal very high resolution remote sensing images, Signal Processing, Vol.124, PP.184-197.

• Zhong, Y., Fe, F, Zhang, L., 2016, Large patch convolutional neural networks for the scene classification of high spatial resolution imagery, Journal of Applied Remote Sensing, Vol.10, No.2, PP.025006.

• Liu, R., Du, B., Zhang, L., 2016, Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF, Remote Sens., Vol.8, No. 6, PP.464.

• Zhong, Y., Wang, X., Zhao, L., Feng, R., Zhang, L., Xu, Y., 2016, Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.119, PP. 49-63.

• Zhao, J., Zhong, Y., Shu, H., Zhang, L., 2016, High-Resolution Image Classification Integrating Spectral-Spatial-Location Cues by Conditional Random Fields, IEEE Trans. on Image Processing,Vol. 25, No. 9, PP.4033-4045.

• Zhu, Q., Zhong, Y., Zhao, B., Xia, G., Zhang, L., 2016, Bag-of-Visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery, IEEE Geoscience and Remote Sensing Letters, Vol.13, No.6, PP.747–751.

• Jiang, C., Zhang, H., Zhang, L., 2016, Hyperspectral Image Denoising with a Combined Spatial and Spectral Weighted Hyperspectral Total Variation Model, CANADIAN JOURNAL OF REMOTE SENSING, Vol. 42, No. 1, PP. 53-72.

• Zhang, F., Zhang, L., Du, B., 2016, Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection, IEEE Trans. on Geoscience and Remote Sensing,Vol. 54, No. 9, PP.5553-5563.

• Zhong, Y., Gao, R., Zhang, L., 2016, Multi-Scale and Multi-Feature Normalized Cuts Segmentation for High Spatial Resolution Remote Sensing Imagery, IEEE Trans. on Geoscience and Remote Sensing,Vol. 54, No. 10,PP.6061-6075.

• Li, J., Yuan, Q., Shen, H., Zhang, L., 2016, Noise Removal From Hyperspectral Image With Joint Spectral-Spatial Distributed Sparse Representation, IEEE Trans. on Geoscience and Remote Sensing,Vol. 54, No. 9, PP.5425-5439.

• Du, B., Xiong, W., Wu, J., Zhang, L., Zhang, L., Tao, D., 2017, Stacked Convolutional Denoising Auto-Encoders for Feature Representation, IEEE Trans. on Cybernetics, Vol.47, No. 4, PP.1017-1027.

• Ma, A., Zhong, Y., Jiao, H., Zhang, L., 2016, Semi-Supervised Subspace-Based DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery, IEEE Trans. on Geoscience and Remote Sensing,Vol. 54, No. 10, PP.6138-6150.

• Zhang, L., Zhang, L., Du, B., 2016, Deep Learning for Remote Sensing Data, IEEE Geoscience and Remote Sensing Magazine,Vol. 4, Issue: 2, PP. 22-40.

• Lu, Q., Huang, X., Li, L., Zhang, L., 2016, A Novel MRF-Based Multifeature Fusion for Classification of Remote Sensing Images, IEEE Geoscience and Remote Sensing Letters, Vol.13, No.4, PP.515-519.

• Huang, X., Han, X., Zhang, L., Gong, J., Liao, W., 2016, Generalized Differential Morphological Profiles for Remote Sensing Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, No. 4, PP.1736-1750.

• Feng, R., Zhong, Y., Wu, Y., He, D, Xu, X., Zhang, L., 2016, Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery, Remote Sens., 8, 250, PP.1-20.

• Zhao, B., Zhong, Y., Zhang, L., 2016, A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.116, PP.73-85.

• Du, B., Zhao, R., Zhang, L., zhang, L., 2016, A spectral-spatial based local summation anomaly detection method for hyperspectral images, Signal Processing, Vo.124, PP. 115-131.

• Li, H., Xu, L., Shen, H., Zhang, L., 2016, A general variational framework considering cast shadows for the topographic correction of remote sensing imagery, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.117, PP.161–171.

• Zhang, H., Zhai, H., Zhang, L., 2016, Spectral-Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images, IEEE Trans. on Geoscience and Remote Sensing, Vol.54, No.6, PP.3672-3684.

• He, W., Zhang, H., Zhang, L., 2016, Sparsity-Regularized Robust Non-Negative Matrix Factorization for Hyperspectral Unmixing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 13, No. 5, PP.686-690.

• He, W., Zhang, H., Zhang, L., Philips,W., Liao,W., 2016,Weighted Sparse Graph Based Dimensionality Reduction for Hyperspectral Images, IEEE Geoscience and Remote Sensing Letters, DOI: 10.1109/LGRS.2016.2536658.

• Ma, X., Shen, H., Zhao, X., Zhang, L., 2016, SAR Image Despeckling by the use of Variational Methods with Adaptive Nonlocal Functionals, IEEE Trans. on Geoscience and Remote Sensing, Vol. 54, No. 6, PP. 3421-3435.

• Liu, X., Lu, X., Shen, H., Yuan, Q., Jiao, Y., Zhang, L., 2016, Stripe Noise Separation and Removal in Remote Sensing Images by Consideration of the Global Sparsity and Local Variational Properties, IEEE Trans. on Geoscience and Remote Sensing, Vol. 54, No. 5, PP.3049-3060.

• Zhao, B., Zhong, Y., Zhang, L., Huang, B., 2016, The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification, Remote Sens., Vol.8, 157.

• Li, H., Wang, X., Shen, H., Yuan, Q., Zhang, L., 2016, An efficient multi-resolution variational Retinex scheme for the radiometric correction of airborne remote sensing images, International Journal of Remote Sensing, Vol. 37, No. 5, 1154–1172.

• Lu, Q., Huang, X., Liu, T., zhang, L., 2016, A structural similarity-based label-smoothing algorithm for the post-processing of land-cover classification, REMOTE SENSING LETTERS, Vol. 7, No. 5, 437–445.

• Wang, Z., Du, B., Zhang, L., Zhang, L., 2016, A batch-mode active learning framework by querying discriminative and representative samples for hyperspectral image classification, Neurocomputing, Vol.179, 29, PP. 88–100.

• Ma, A., Zhong, Y., Zhang, L., 2016, Spectral-Spatial Clustering with a Local Weight Parameter Determination Method for Remote Sensing Imagery, Remote Sens., Vol.8,No. 2, PP. 1-22.

• Yang, F., Xia, G., Liu, G., Zhang, L., Huang, X., 2016, Dynamic texture recognition by aggregating spatial and temporal features via ensemble SVMs, Neurocomputing, Vol.173, PP. 1310-1321.

• Hang, C., Zhang, H., Gao, C., Jiang, C., Song, N., Zhang, L., 2016, A Remote Sensing Image Fusion Method Basedon the Analysis Sparse Model, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.9, No.1, PP.439-453.

• Guo, X., Huang, X., Zhang, L., Benediktsson, J. A., 2016, Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery, IEEE Trans. on Geoscience and Remote Sensing, Vol. 54, No. 6, PP. 3248-3264.

• Du, B., Wang, Z., Zhang, L, Zhang, L., Liu, W, Shen, J., Tao, D., 2016, Exploring Representativeness and Informativeness for Active Learning, IEEE Trans. on Cybernetics, DOI: 10.1109/TCYB.2015.2496974.

• Feng, R., Zhong, Y., Xu, X., Zhang, L., 2016, Adaptive Sparse Subpixel Mapping With a Total Variation Model for Remote Sensing Imagery , IEEE Trans. on Geoscience and Remote Sensing, Vol. 54, No. 5, PP.2855-2872.

• Shen, H.,Huang, L., Zhang, L., Wu, P., Zeng, C., 2016, Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of Wuhan in China, Remote Sensing of Environment, Vol.172, PP.109-125.

• Zhao, B., Zhong, Y., Xia, G.-S., Zhang, L., 2016, Dirichlet-Derived Multiple Topic Scene Classification Model Fusing Heterogeneous Features for High Resolution Remote Sensing Imagery, IEEE Trans. on Geoscience and Remote Sensing, Vol. 54, No. 4, PP.2108-2123.

• Huang, X., Weng, C., Lu, Q., Feng, T., Zhang, L., 2015, Automatic Labelling and Selection of Training Samples for High-Resolution Remote Sensing Image Classification over Urban Areas, Remote Sens., Vol.7,No. 12, PP. 16024-16044.

• Hu, J., Xia, G.-S., Hu, F., Zhang, L., 2015, A Comparative Study of Sampling Analysis in the Scene Classification of Optical High-Spatial Resolution Remote Sensing Imagery, Remote Sens., Vol.7,No. 11, PP. 14988-15013.

• Xia, G.-S., Wang, Z., Xiong, C., Zhang, L., 2015, Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge, Remote Sens., Vol.7, No. 11, PP. 15014-15045.

• Hu, F., Xia, G.-S., Hu, J., Zhang, L., 2015, Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery, Remote Sensing, Vol. 7, No. 11, PP. 14680-14707.

• Xia, G.-S., Liu, G., Yang, W., Zhang, L., 2015, Meaningful objects segmentation from SAR images via a multi-scale non-local active contour model, IEEE Trans. on Geoscience and Remote Sensing, Vol.54, No.3, PP.1860-1873.

• Wu, C., Zhang, L., Zhang, L., 2015, A Scene Change Detection Framework for Multi-Temporal Very High Resolution Remote Sensing Images, Signal Processing, Vol.124, PP.184-197.

• Zhang, F., Du, B., Zhang, L., 2015, Scene Classification via a Gradient Boosting Random Convolutional Network Framework, IEEE Trans. on Geoscience and Remote Sensing, Vol.54, No.3, PP.1793-1801.

• Zhao, R., Du, B., Zhang, L., Zhang, L., 2015, Beyond Background Feature Extraction: An Anomaly Detection Algorithm Inspired by Slowly Varying Signal Analysis, IEEE Trans. on Geoscience and Remote Sensing, Vol.54, No.3, PP.1757-1775.

• Zhang, Y., Du, B., Zhang, L., Wang, S., 2015, A Low-Rank and Sparse Matrix Decomposition Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection, IEEE Trans. on Geoscience and Remote Sensing, Vol.54, No.3, PP.1376-1389.

• Xu, M., Zhang, L., Du, B., Zhang, L., 2016, An Image-Based Endmember Bundle Extraction Algorithm Using Reconstruction Error for Hyperspectral Imagery, Neurocomputing, Vol.173, Part 2, PP. 397–405.

• Wen, D., Huang, X, Zhang, L., 2016, A Novel Automatic Change Detection Method for Urban High-resolution Remotely Sensed Imagery Based on Multi-index Scene Representation, IEEE Trans. on Geoscience and Remote Sensing, Vol.54, No.1, PP.609-625.

• Zhong, Y., Cui, M., Zhu, Q., Zhang, L., 2015, Scene classification based on multifeature probabilistic latent semantic analysis for high spatial resolution remote sensing images, J. Appl. Remote Sens, Vol.9, No. 095064, DOI: 10.1117/1.JRS.9.095064.

• Li, X., Hui, N., Shen, H., Fu, Y., Zhang, L., 2015, A robust mosaicking procedure for high spatial resolution remote sensing images, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.109, PP.108-125.

• Dong, Y., Zhang, L., Zhang, L., Du, B., 2015, Maximum margin metric learning based target detection for hyperspectral images, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.108, PP.138-150.

• He, W., Zhang, H., Zhang, L., Shen, H., 2016, Total Variation Regularized Low-rank Matrix Factorization for Hyperspectral Image Restoration, IEEE Trans. on Geoscience and Remote Sensing, Vol.54, No.1, PP.176-188.

• Hu, F., Xia, G., Wang, Z., Huang, X., Zhang, L., 2015, Unsupervised Feature Learning via Spectral Clustering of Multi-dimensional Patches for Remotely Sensed Scene Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.8, No.5, PP.2015-2030.

• Yue, L., Shen, H., Zhang, L., 2015, Fusion of Multi-scale DEMs using a Regularized Super-resolution Method, International Journal of Geographical Information Science, Vol. 29, No. 12, PP. 2095–2120.

• Wu, B., Huang, B., Zhang, L., 2015, An Error Bound Regularized Sparse Coding for Spatiotemporal Reflectance Fusion,IEEE Trans. on Geoscience and Remote Sensing, Vol.53, No.12, PP.6791-6803.

• Li, J., Zhang, H., Zhang, L., Shen, H., Du, Q., 2015, Urban Classification by the Fusion of Thermal Infrared Hyperspectral and Visible Data, Photogrammetric Engineering & Remote Sensing, Vol.81, No.12, PP. 901–911.

• Shen,H., Peng, L.,Yue, L., Yuan, Q., Zhang, L., 2015, Adaptive Norm Selection for Regularized Image Restoration and Super-Resolution, IEEE Transactions on Cybernetics, Vol.46, No. 6, PP.1388-1399.

• Li,J., Zhang, H., Zhang, L., Ma, L., 2015, Hyperspectral Anomaly Detection by the Use of Background Joint Sparse Representation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 6, PP.2523-2533.

• Zhong, Y., Zhu, Q., Zhang, L., 2015, Scene Classification Based on Multi-Feature Fusion Probability Topic Model for High Spatial Resolution Remote Sensing Imagery,IEEE Trans. on Geoscience and Remote Sensing, Vol. 53, No. 11, PP.6207-6222.

• Yang, G., Shen, H., Zhang, L., He, Z., Li, X., 2015, A Moving Weighted Harmonic Analysis Method for Reconstructing High-quality SPOT VEGETATION NDVI Time-series Data,IEEE Trans. on Geoscience and Remote Sensing, Vol. 53, No. 11, PP.6008-6021.

• Shen, H., Li, X., Cheng, Q., Zeng, C., Yang, G., Li, H., and Zhang, L., 2015, Missing Information Reconstruction of Remote Sensing Data: A Technical Review, IEEE Geoscience and Remote Sensing Magazine, Vol.3, No.3, PP. 61-85.

• Zhang, H., Zhang, L., Shen, H., 2015,A Blind Super-resolution Reconstruction Method Considering Image Registration Errors, International Journal of Fuzzy Systems, Vol.17, No.2, PP.353-364.

• Chen, N., Xing, C., Zhang, X., Zhang, L., Gong, J., 2015, Spaceborne Earth-Observing Optical Sensor Static Capability Index for Clustering,IEEE Trans. on Geoscience and Remote Sensing, Vol.53, No.10, PP.5504-5518.

• Shi, Q., Du, B., Zhang, L., 2015, Domain Adaptation for Remote Sensing Image Classification: a Low-Rank Reconstruction and Instance Weighting Label Propagation Inspired Algorithm, IEEE Trans. on Geoscience and Remote Sensing,Vol.53, No.10, PP.5677-5689.

• Li, X., Shen, H., Zhang, L., Li, H., 2015, Sparse-based reconstruction of missing information in remote sensing images from spectral/temporal complementary information,ISPRS Journal of Photogrammetry and Remote Sensing, Vol.106, PP.1-15.

• Huang,X., Xie, C., Zhang, L., 2015, Combining pixel- and object-based machine learning for identification of water body types from urban high-resolution remote-sensing imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.8, No.5, PP.2097-2110.

• Sun, W., Zhang, L., Du, B., 2015, Band Selection Using Improved Sparse Subspace Clustering for Hyperspectral Imagery Classification, IEEE Journal of Selected Topics in Applied Earth Observations and remote sensing, Vol. 8, No. 6, PP.2784-2797.

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•  Huang, X., Zhang, L., 2012, A multilevel decision fusion approach for urban mapping using very-high-resolution multi/hyper-spectral imagery, Int.J.Remote Sensing, Vol.33, No.11, PP. 3354-3372.

•  Huang, X., Zhang, L., 2012, A multiscale urban complexity index based on 3D wavelet transform for spectral–spatial feature extraction and classification: an evaluation on the 8-channel WorldView-2 imagery, Int.J.Remote Sensing, Vol.33, No.8, PP. 2641-2656.

•  Li, H., Zhang, L., Shen, H., Li, P., 2012, A Variational Gradient-based Fusion Method for Visible and SWIR Images, Photogrammetric Engineering & Remote Sensing, Vol.78, No. 9, PP. 947-958.

•  Zhang, H., Zhang, L., Shen, H., 2012, Multi-frame Super-Resolution Algorithm for Hyperspectral Images Signal Processing, Signal Processing , Vol.92, No.9, PP. 2082-2096.

•  Huang, X., Zhang, L., 2012, Morphological Building/Shadow Index for Building Extraction from High-Resolution Imagery Over Urban Areas, IEEE Journal of Selected Topics in Earth Observations and Remote Sensing , Vol. 5, No.1, PP. 161-172.

•  Jiang, C., Zhang, H., Shen, H., Zhang, L., 2012, A Practical Compressed Sensing-Based Pan-Sharpening Method, IEEE Geoscience and Remote Sensing Letters, Vol.9, No.4, PP.629-633.

•  Zhong, Y., Zhang, L., 2012, Remote Sensing Image Sub-pixel Mapping based on Adaptive Differential Evolution, IEEE Trans. on Systems, Man and Cybernetics, Part B , Vol.42, No.5, PP.1306-1329.

•  Wu, k., Zhang, L., Niu, R., Du, B., 2011, Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery, Optical Engineering, Vol.50, No.12, PP.12 6201-1-14 .

•  Fan, Q., Wang, S., Zhang, L., 2012, Recurrence in β -expansion over formal Laurent series, Monatsh Math, 166:379-394.

•  Li, H., Zhang, L., Shen, H., 2012, A Perceptually Inspired Variational Method for the Uneven Intensity Correction of Remote Sensing Images, IEEE Trans. on Geoscience and Remote Sensing, Vol.22, No.8, PP.3053 - 3065.

•  Shen, H., Du, L., Zhang, L., Gong, W., 2012, A Blind Restoration Method of Remote Sensing Images , IEEE Geoscience and Remote Sensing Letters , Vol.9, No.6, PP.1137 - 1141.

•  Yuan, Q., Zhang, L., Shen, H., 2012, Hyperspectral Image Denoising Employing a Spectral-spatial Adaptive Total Variation Model, IEEE Trans. on Geoscience and Remote Sensing , Vol.50, No.10, PP.3660-3677.

•  Yuan, Q., Zhang, L., Shen, H., 2012, Muti-frame Super-resolution Employing a Spatially Weighted Total Variation Model, IEEE Trans. on Circuits and Systems for Video Technology , Vol. 22, No.3, PP. 379-392.

•  Zhang, L., Zhang, L., Tao, D., Huang, X., 2012, On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification, IEEE Trans. on Geoscience and Remote Sensing , Vol. 50, No.3, PP. 879-893.

•  Jiao, H., Zhong, Y., Zhang, L., 2012, Artificial DNA Computing-Based Spectral Encoding and Matching Algorithm for Hyperspectral Remote Sensing Data, IEEE Trans. on Geoscience and Remote Sensing , Vol.50, No.10, PP.4085-4104.

•  Wang, Y., Niu, R., Zhang, L., Wu, K, 2011, A Scale-based Forward-and-Backward Diffusion Process for Adaptive Image Enhancement and Denoising, EURASIP Journal On Advances In Signal Processing , Art. No. 22, DOI:10.1186/1687-6180-2011-22.

•  Zhong, Y., Zhang, L., 2012, An Adaptive Artificial Immune Network for Supervised Classification of Multi/Hyper-Spectral Remote Sensing Imagery, IEEE Trans. on Geoscience and Remote Sensing, Vol. 50, No.3, PP. 894-909.

•  Zhong, Y., Zhang, L., 2011, New Fuzzy Clustering Algorithm based on Clonal Selection for Land Cover Classification, Mathematical Problems in Engineering , 708459.

•  Huang, X., Zhang, L., 2011, A Multidirectional and Multiscale Morphological Index for Automatic Building Extraction from Mutispectral GeoEye-1 Imagery, Photogrammetric Engineering and Remote Sensing, Vol.77, No.7 , PP.721-732.

•  Zhong, Y., Zhang, L., 2011, Unsupervised Remote Sensing Image Classification using an Artificial Immune Network, Int.J.Remote Sensing, Vol.32, No.19, PP.5461-5483.

•  Tang, Y, Zhang, L., Huang, X., 2011, Object-oriented change detection based on the K-S test using high-resolution multispectral imagery, Int.J.Remote Sensing, Vol.32,No.20, PP.5719-5740.

•  Zhang, Q., Zhang, L., Huang, X., 2011, Classification of high spatial resolution imagery based on distance-weighted MRF with an improved ICM method, Int.J.Remote Sensing,Vol.32, No.24, PP.9843-9868.

•  Li, H., Zhang, L., 2011, A Hybrid Automatic Endmember Extraction Algorithm Based On A Local Window, IEEE Trans. on Geoscience and Remote Sensing,Vol.49, No.11, PP.4223-4237 .

•  Yue, P., Wei, Y., Di, L., He d, L., Gong, J., Zhang, L., 2011, Sharing geospatial provenance in a service-oriented environment, Computers, Environment and Urban Systems, Vol. 35, Issue 4, PP. 333-343.

•  Huang , X., Zhang , L., 2011 , Information fusion of aerial images and LIDAR data in urban areas: vector stacking, re-classification, and post-processing approaches, Int.J.Remote Sensing, 32 , No, 1, PP. 69 - 84.

•  Zhang, L., Yuan, Q., Shen, H., Li, P., 2011, Multiframe image super-resolution adapted with local spatial information, J. Opt. Soc. Am. A, Vol. 28, Issue 3, PP. 381-390 .

•  Zhang, L., Zhang, L., Tao, D., Huang, X., 2011 , A Multi-feature Tensor for Remote Sensing Target Identification, IEEE Geoscience and Remote Sensing Letters , Vol.8, No.2, pp374-378.

•  Shen, H., Zeng, C., Zhang, L., 2011 , Recovering Reflectance and Radiance of AQUA MODIS Band 6 Based on Within-Class Local Fitting, IEEE Journal of Selected Topics in Earth Observations and Remote Sensing, Vol.4, No.1,185-192.

•  Chen, T., Niu, R., Wang, Y., Li, P., Zhang, L., 2011 , Assessment of spatial distribution of soil loss over the upper basin of Miyun reservoir in China based on RS and GIS techniques. Environmental Monitoring and Assessment, Vol.179, No.1-4, 605-617.

•  Chen, T., Niu, R., Li, P., Zhang, L., 2011 , Regional soil erosion risk mapping using RUSLE, GIS and Remote Sensing: A case study in Miyun Watershed, North China, Environmental Earth Sciences , Vol.63, No.3, 533-541.

•  Du, B., Zhang, L., 2011 , Random Selection based Anomaly Detector for Hyperspectral Imagery, IEEE Trans. on Geoscience and Remote Sensing, Vol.49, PP.1578-1589 .

•  Yuan, Q., Zhang, L., Shen, H., Li, P., 2010, Adaptive multiple-frame image super-resolution based on U-curve, IEEE Trans. on Image Processing ,Vol.19,No.12,PP.3157-3170.

•  Wang, Y., Niu, R., Zhang, L., 2010, Region-based Adaptive Anisotropic Diffusion for Image Enhancement and Denoising, Optical Engineering , Vol. 49 , No.11, 117007.

•  Wang, y., Niu, R., Yu, X., Zhang, L., 2010, Image restoration and enhancement based on tunable forward-and-backward diffusion, Optical Engineering, Vol.49, No.5, 057003.

•  Huang , X., Zhang , L., 2010 , Comparison of Vector Stacking, Multi-SVMs Fuzzy Output, and Multi-SVMs Voting Methods for Multiscale Urban Mapping, IEEE Geoscience and Remote Sensing Letters, Vol.7, No.2, 261-265.

•  Zhang , L., Huang , X., 2010 , Object-Oriented Subspace Analysis for Airborne Hyperspectral Remote Sensing Images , Neurocomputing, 73 , 927–936.

•  Zhang, L., Du, B., Zhong, Y., 2010, Hybrid Detectors based on Selective Endmembers, IEEE Trans. on Geoscience and Remote Sensing, Vol. 48, No. 6 , PP.2633–2646.

•  Zhang, L., Zhang, H., Shen, H., Li, P., 2010, A Super-resolution Reconstruction Algorithm for Surveillance Images, Signal Processing, 90 , 848–859.

•  Huang, W., Zhang, L., Furumi, S., Muramutsu, K., Daigo, M., Li, P., 2010 , Topographic Effects on Estimating Net Primary Productivity of Green Coniferous Forest in Complex Terrain using Landsat Data: A Case Study of Yoshino Mountain, Japan, Int.J.Remote Sensing, 31 , No.11, PP.2941-2957.

•  Zhang , L., Huang , X., 2009 , Advanced processing techniques for remotely sensed imagery, J.Remote Sensing, Vol.13, No.4, PP.559-569. (Invited paper)

•  Huang , X., Zhang , L., 2009 , A Comparative Study of Spatial Approaches for Urban Mapping using hyperspectral ROSIS images over Pavia City, northern of Italy, Int.J.Remote Sensing , 30, No. 12 , PP.3205 – 3221.

•  Huang , X., Zhang , L., 2009 , Road centreline extraction from high resolution imagery based on multiscale structural features and support vector machines , Int.J.Remote Sensing, 30, No.8, PP.1977-1987.

•  Huang, X., Zhang , L., Wang, L. , 2009 , Evaluation of Morphological Texture Features for Forest Species Discrimination using IKONOS Multispectral Imagery, IEEE Geoscience and Remote Sensing Letters, Vol.6, No.3 , PP. 393 –397 .

•  Shen, H., Zhang, L., 2009, A MAP-Based Algorithm for Destriping and Inpainting of Remotely Sensed Images , IEEE Trans. on Geoscience and Remote Sensing, Vol. 47, No. 5 , PP.1492 –1502.

•  Shen, H., Ng, M. K., Li, P., Zhang, L., 2009, Super-resolution Reconstruction Algorithm to MODIS Remote Sensing Images, The Computer Journal, Vol.52, No.1 , PP. 90 –100 .

•  Huang , X., Zhang , L., 2008 , An Adaptive Mean Shift Analysis Approach for Object Extraction and Classification from Urban Hyperspectral Imagery , IEEE Trans. on Geoscience and Remote Sensing , Vol.46, No.12, 4173-4185.

•  Zhang , L., Wu, K., Zhong , Y., Li, P. , 2008, A new sub-pixel mapping algorithm based on a BP neural network with an observation model, Neurocomputing, Vol.71 , N.10-12, PP.2046-2054.

•  Huang , X., Zhang , L., Li, P. , 2008 , A multiscale feature fusion approach for classification of very high resolution satellite imagery, Int.J.Remote Sensing,29, No. 20 ,PP. 5923 – 5941.

•  Huang , X., Zhang , L., Li, P. , 2008 , Classification of very high spatial resolution imagery based on the fusion of edge and multispectral information, Photogrammetric Engineering and Remote Sensing , Vol.74, No.12 , PP. 1585 –1596.

•  Zhang , L., Zhao , Y., Huang, B. , Li, P. , 2008 , Texture Feature Fusion with Neighborhood Oscillating Tabu Search for High Resolution Image, Photogrammetric Engineering and Remote Sensing, Vol.74, No.3 , PP.323-332 .

•  Huang , X., Zhang , L., Li, P. , 2007 , An adaptive multiscale information fusion approach for feature extraction and classification of very high resolution satellite imagery, IEEE Geoscience and Remote Sensing Letters, Vol.4, No.4, PP.654-658.

•  Zhong , Y., Zhang , L., Gong, J., Li, P. , 2007, A Supervised Artificial Immune Classifier for Remote Sensing Imagery, IEEE Trans. On Geoscience and Remote Sensing, Vol.45, No.12, PP.3957-3966.

•  Wang , Y., Zhang , L., Li, P. , 2007, A Scale-Based Forward-and-Backward Diffusion Algorithm for Image Enhancement and Noise Reduction, IEEE Trans. on Image Processing, Vol.16, No.7, PP.1854-1864 .

•  Ng, M. K., Shen, H., Lam , E., Zhang , L., 2007 , A Total Variation Based Super-Resolution Reconstruction Algorithm for Digital Video , EURASIP Journal On Advances In Signal Processing: Art. No. 74585 2007.

•  Niu, R., Zhang, L., Shao, Z., Chen, Q., 2007, Web-based Geological Hazard Monitoring in Three Gorges, Photogrammetric Engineering and Remote Sensing, Vol.73, No.6 , PP.707 - 719 .

•  Huang , X., Zhang , L., Li, P. , 2007 , Classification and Extraction of Spatial Features in Urban Areas using High Resolution Multispectral Imagery , IEEE Geoscience and Remote Sensing Letters, Vol.4, No.2 , PP.260 - 264 .

•  Zhao , Y., Zhang , L., Li, P. , 2007 , Classification of High Spatial Resolution Imagery Using Improved General Markov Random Field – Based Texture Features, IEEE Trans. On Geoscience and Remote Sensing, Vol.45, No.5, PP.1458 -1468 .

•  Zhang, L., Wu, B., Huang, B., Li, P., 2007, Nonlinear Estimation of Subpixel Proportion Via Kernel Least Square Regression, Int.J.Remote Sensing, 28, No.18, PP. 4157 – 4172.

•  Zhang, L., Zhang, L., Yan, L., Yang, S., Fujiwara, N., Murumatsu, K., Daigo, M., 2007 , Hyperspectral data transformation and vegetation index performance based on the universal pattern decomposition method , Journal of Imaging Science and Technology, Vol.51, No.2 PP.141-147 .

•  Zhang , L., Zhong , Y. , Huang, B. , Li, P. , 2007, Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery, IEEE Trans. On Geoscience and Remote Sensing, Vol.45, No.12, PP.4172-4185.

•  Zhang , L., Zhong , Y., Huang, B. , Li, P. , 2007, A Resource Limited Artificial Immune Algorithm for Supervised Classification of Multi/Hyper-Spectral Remote Sensing Image, Int.J.Remote Sensing , Vol. 28, No.7, PP.1665 - 1686 .

•  Shen, H., Zhang , L., Huang, B. , Li, P. , 2007, A MAP Approach for Joint Motion Estimation, Segmentation and Super Resolution, IEEE Trans. on Image Processing, Vol.16, No.2,PP.479-490.

•  Zhang, L., Furumi, S., Murumatsu, K., Fujiwara, N., Daigo, M., and Zhang, L., 2007, A New Vegetation Index Based on the Universal Pattern Decomposition Method, Int.J.Remote Sensing, 28(1):107-124.

•  Zhang, L., Fujiwara, N., Furumi, S., Muramatsu, K., Daigo, M., and Zhang, L, 2007, Assessment of the universal pattern decomposition method using MODIS and ETM+ data, Int.J.Remote Sensing, 28(1):125-142.

•  Zhang, L., Furumi, S., Murumatsu, K., Fujiwara, N., Daigo, M., and Zhang, L., 2006, Sensor-independent analysis method for hyper-multi spectral data based on the pattern decomposition method, Int.J.Remote Sensing, 27(21): 4899-4910.

•  Zhang , L., Huang , X., Huang, B. , Li, P. , 2006 , A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery , IEEE Trans. on Geoscience and Remote Sensing, Vol.44, No.10, PP. 2950–2961 .

•  Wu, B., Zhang, L., Li, P., 2006, Nonlinear Estimation of Hyperspectral Mixture Pixel Proportion Based on Kernel Orthogonal Subspace Projection, Lecture Notes In Computer Science 3971: 1070-1075 Part 1: Advanced In Neural Networks .

•  Zhong , Y., Zhang , L., Huang, B. , Li, P. , 2006, An Unsupervised Artificial Immune Classifier for Multi/hyper-spectral Remote Sensing Image, IEEE Trans. on Geoscience and Remote Sensing, Vol.44, No.2, PP. 420–431 .

•  Zhang, L., Li, D., 1999, Artificial neural network application in spectral recognition, Spectroscopy and Spectral Analysis 19 (2): 158-160 .

•  Zhang, L., Li, D., Tong, Q., Zheng, L., 1998, A study of the spectral mixture model of soil and vegetation in Poyang lake area, China, Int.J.Remote Sensing, Vol.19, PP. 2077-2084.

出书 Book

•  Zhang , L., Zhong , Y., 2009, Analysis of Hyperspectral Remote Sensing images, Geospatial Technology for Earth Observation, Springer.

•  张良培、杜博、张乐飞,2012,高光谱遥感图像处理, 科学出版社

•  张良培、沈焕峰、张洪艳、袁强强,2012,图像超分辨率重建,科学出版社L. Zhang, et al., 2012, Image Super-resolution Technologies, Science Press (China).

•  张良培、张立福, 2011 ,高光谱遥感, 测绘出版社 L. Zhang, et al., 2011, Hyperspectral Remote Sensing (textbook), Surveying and Mapping Press (China).

•  张良培、张立福, 2005 ,高光谱遥感, 武汉大学出版社 L. Zhang, et al., 2005, Hyperspectral Remote Sensing (textbook), Wuhan University Press (China).

• L. Zhang, et al., 2014, Hyperspectral Remote Sensing Images Processing, Science Press (China).

 

专利 Patents

•  一种自适应变分遥感影像融合方法, 201010227696.3. L. Zhang, et al., 2010, An Adaptive Variation Method of Remote Sensing Image Fusion.

•  一种推扫式卫星影像 CCD 相对辐射校正方法 200410060986.8

•  一种遥感影像的人工免疫监督分类方法 200610019506.2 L. Zhang, et al., 2006, An Artificial Immune Surveillance Method for Remote Sensing Image Classification.

•  一种遥感影像的人工免疫特征选择方法 200610019507.7 L. Zhang, et al., 2006, An Artificial Immune Feature Selection Method for Remote Sensing Images.

•  一种遥感影像的人工免疫非监督分类方法 200610019508.1

•  一种可调节的光谱和空间特征混合分类方法 200610124494.X

•  L. Zhang, et al., 2013, Hyperspectral Imagery Restoration With Band Clustering and Sparse Representation.

•  L. Zhang, et al., 2013, A Piece-Wise Approach to Removing the Stripes of Remote Sensing Image.

•  L. Zhang, et al., 2013, Thick Cloud Removal for Remotely Sensed Images Using Multi-Temporal Data.

•  L. Zhang, et al., 2013, A Restoration Method Using Multi-temporal Weighted Regression for Remote Sensing Imagery.

• L. Zhang, et al., 2011, A DNA Based Spectral Matching Method for Hyperspectral Remote Sensing Image Classification.

•  L. Zhang, et al., 2011, Hyperspectral Remote Sensing Image Sub-Pixel Mapping Based on Clonal Selection.

•  L. Zhang, et al., 2010, A Hyperspectral Remote Sensing Anomaly Detection Method.

•  L. Zhang, et al., 2010, Hyperspectral Imaging Target Detection Based on Selective Endmembers.

•  L. Zhang, et al., 2007, A Hybrid Spectral and Spatial Classification Method for Remote Sensing Images.

•  L. Zhang, et al., 2007, Shape of Spatial Feature Extraction and Classification for Remote Sensing Images.