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基于逐像素递归处理的高光谱实时亚像元目标检测 (Real-time Sub-pixel Object Detection for Hyperspectral Image Based on Pixel-by-pixel Processing).

W. Lin, L. Zhao, and X. Li. 计算机科学, 45 (6): 259-264 (2018)

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Li Li

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A novel Bayesian lasso model based on spatial-correlated sparsity for semisupervised hyperspectral unmixing.H. Jiao, R. Huang, X. Li, and L. Zhao. IGARSS, page 5434-5437. IEEE, (2017)Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification.Z. Cao, X. Li, and L. Zhao. CoRR, (2020)Small Sample Building Energy Consumption Prediction Using Contrastive Transformer Networks.W. Ji, Z. Cao, and X. Li. Sensors, 23 (22): 9270 (November 2023)Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction.J. Li, X. Li, L. Wang, and L. Zhao. Frontiers Inf. Technol. Electron. Eng., 17 (3): 250-257 (2016)基于逐像素递归处理的高光谱实时亚像元目标检测 (Real-time Sub-pixel Object Detection for Hyperspectral Image Based on Pixel-by-pixel Processing).W. Lin, L. Zhao, and X. Li. 计算机科学, 45 (6): 259-264 (2018)Kernel-OPBS Algorithm: A Nonlinear Feature Selection Method for Hyperspectral Imagery.X. Li, W. Zhang, S. Niu, Z. Cao, and L. Zhao. IEEE Geosci. Remote. Sens. Lett., 17 (3): 464-468 (2020)Novel Design of Decision-Tree-Based Support Vector Machines Multi-class Classifier.L. Zhao, X. Li, and G. Zhao. ICIC (2), volume 4682 of Lecture Notes in Computer Science, page 871-880. Springer, (2007)A Bathymetry Mapping Approach Combining Log-Ratio and Semianalytical Models Using Four-Band Multispectral Imagery Without Ground Data.H. Xia, X. Li, H. Zhang, J. Wang, X. Lou, K. Fan, A. Shi, and D. Li. IEEE Trans. Geosci. Remote. Sens., 58 (4): 2695-2709 (2020)Hyperspectral Unmixing Using Robust Deep Nonnegative Matrix Factorization.R. Huang, H. Jiao, X. Li, S. Chen, and C. Xia. Remote. Sens., 15 (11): 2900 (2023)Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows.L. Zhao, W. Lin, Y. Wang, and X. Li. Remote. Sens., 10 (1): 103 (2018)

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Li Li

Li Li

Li Li

Li Li

Li Li

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