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面向高光谱分类的局部几何稀疏保持嵌入

黄鸿 唐玉枭 段宇乐

黄鸿, 唐玉枭, 段宇乐. 面向高光谱分类的局部几何稀疏保持嵌入. 自动化学报, 2022, 48(10): 2496−2507 doi: 10.16383/j.aas.c190594
引用本文: 黄鸿, 唐玉枭, 段宇乐. 面向高光谱分类的局部几何稀疏保持嵌入. 自动化学报, 2022, 48(10): 2496−2507 doi: 10.16383/j.aas.c190594
Huang Hong, Tang Yu-Xiao, Duan Yu-Le. Local geometry and sparsity preserving embedding for hyperspectral image classification. Acta Automatica Sinica, 2022, 48(10): 2496−2507 doi: 10.16383/j.aas.c190594
Citation: Huang Hong, Tang Yu-Xiao, Duan Yu-Le. Local geometry and sparsity preserving embedding for hyperspectral image classification. Acta Automatica Sinica, 2022, 48(10): 2496−2507 doi: 10.16383/j.aas.c190594

面向高光谱分类的局部几何稀疏保持嵌入

doi: 10.16383/j.aas.c190594
基金项目: 国家自然科学基金(42071302), 重庆市基础研究与前沿探索项目(cstc2018jcyjAX0093)资助
详细信息
    作者简介:

    黄鸿:重庆大学光电工程学院教授. 主要研究方向为流形学习, 模式识别, 遥感影像智能化处理. 本文通信作者. E-mail: hhuang@cqu.edu.cn

    唐玉枭:重庆大学光电工程学院硕士研究生. 主要研究方向为模式识别和图像处理.E-mail: tangyuxiao@cqu.edu.cn

    段宇乐:重庆大学光电工程学院博士研究生. 主要研究方向为机器学习, 模式识别和遥感图像处理.E-mail: duanyule@cqu.edu.cn

Local Geometry and Sparsity Preserving Embedding for Hyperspectral Image Classification

Funds: Supported by National Natural Science Foundation of China (42071302) and Chongqing Basic Research and Frontier Exploration Project (cstc2018jcyjAX0093)
More Information
    Author Bio:

    HUANG Hong Professor at the College of Optoelectronic Engineering, Chongqing University. His research interest covers manifold learning, pattern recognition, and intelligent processing of remote sensing images. Corresponding author of this paper

    TANG Yu-Xiao Master student at the College of Optoelectronic Engineering, Chongqing University. His research interest covers pattern recognition and image processing

    DUAN Yu-Le Ph.D. candidate at the College of Optoelectronic Engineering, Chongqing University. His research interest covers machine learning, pattern recognition, and remote sensing image processing

  • 摘要: 大量维数约简(Dimensionality reducion, DR)方法表明保持数据间稀疏特性的同时, 确保几何结构的保持能更有效提取出具有鉴别性的特征, 为此本文提出一种联合局部几何近邻结构和局部稀疏流形的维数约简方法. 该方法首先通过局部线性嵌入方法重构每个样本以保持数据的局部线性关系, 同时计算样本邻域内的局部稀疏流形结构, 在此基础上通过图嵌入框架保持数据的局部几何近邻结构和稀疏结构, 最后在低维嵌入空间中使类内数据尽可能聚集, 提取低维鉴别特征, 从而提升地物分类性能. 在Indian Pines和PaviaU高光谱数据集上的实验结果表明, 本文方法相较于传统维数约简方法能明显提高地物的分类性能, 总体分类可达到83.02%和91.20%, 有利于实际应用.
  • 图  1  局部几何稀疏保持嵌入(LGSPE)方法流程图

    Fig.  1  Flowchart of the LGSPE method

    图  2  Indian Pines高光谱图像

    Fig.  2  Indian Pines hyperspectral image

    图  3  PaviaU 高光谱图像

    Fig.  3  PaviaU hyperspectral image

    图  4  不同参数$\lambda $$\beta $在两个数据集上的总体分类精度

    Fig.  4  OAs with different parameters$\lambda $and$\beta $on two datasets

    图  5  不同参数${S_n}$${C_n}$在两个数据集上的总体分类精度

    Fig.  5  OAs with different parameters ${S_n}$ and ${C_n}$ on two datasets

    图  6  各算法在Indian Pines数据集上的分类结果图

    Fig.  6  Classification maps of different method on Indian Pines dataset

    图  7  PaviaU数据集上各算法的分类结果图

    Fig.  7  Classification maps of different method on PaviaU dataset

    表  1  不同算法在Indian Pines数据集上的分类结果(总体分类精度±标准差(%) ($p,h$))

    Table  1  Classification results with different methods on Indian Pines dataset (OA ± Std (%) ($p,h$))

    算法2%4%6%8%10%
    RAW58.08±1.01 (1.5×10−08, 1)63.17±0.95 (5.8×10−12, 1)64.97±0.65 (1.2×10−14, 1)66.11±0.57 (2.2×10−17, 1)67.88±0.55 (3.9×10−20, 1)
    PCA58.02±1.03 (1.1×10−08, 1)62.99±1.04 (8.7×10−12, 1)64.83±0.53 (4.6×10−15, 1)66.09±0.70 (7.2×10−17, 1)67.80±0.53 (2.3×10−20, 1)
    LDA47.39±1.49 (5.9×10−16, 1)63.15±1.47 (1.5×10−09, 1)68.52±1.19 (1.3×10−09, 1)71.05±1.03 (7.5×10−11, 1)73.53±0.63 (2.2×10−14, 1)
    LFDA57.68±0.80 (2.3×10−10, 1)63.00±1.26 (2.1×10−11, 1)65.17±0.84 (9.3×10−14, 1)67.25±0.63 (3.5×10−16, 1)68.78±0.79 (1.5×10−17, 1)
    SNPE47.29±1.82 (1.5×10−14, 1)65.04±1.29 (2.1×10−08, 1)68.72±1.28 (2.3×10−09, 1)70.19±0.73 (6.2×10−13, 1)71.67±0.44 (1.7×10−17, 1)
    SPP51.95±1.04 (8.0×10−14, 1)60.00±0.80 (1.6×10−15, 1)64.29±0.96 (6.2×10−15, 1)65.98±0.59 (2.8×10−17, 1)67.97±0.55 (5.2×10−20, 1)
    DLSP57.47±1.36 (2.1×10−06, 1)63.63±1.18 (1.5×10−10, 1)66.05±0.58 (7.6×10−14, 1)67.65±0.74 (1.4×10−15, 1)69.35±0.94 (1.6×10−16, 1)
    SDL57.56±1.08 (8.8×10−09, 1)64.04±0.52 (2.3×10−13, 1)69.05±0.97 (2.4×10−11, 1)71.27±0.88 (9.0×10−11, 1)74.03±0.72 (3.3×10−13, 1)
    DSPE61.66±1.80 (2.5×10−01, 0)65.63±1.89 (5.5×10−06, 1)69.00±1.37 (3.8×10−09, 1)69.25±1.28 (1.7×10−11, 1)70.84±1.15 (3.9×10−13, 1)
    MSME51.66±1.34 (2.4×10−13, 1)57.76±1.38 (1.3×10−14, 1)61.65±0.84 (1.7×10−16, 1)64.36±0.57 (8.0×10−19, 1)66.57±0.99 (4.5×10−18, 1)
    LGSFA58.44±1.64 (3.7×10−06, 1)68.17±1.69 (6.0×10−03, 1)72.77±1.13 (3.4×10−03, 1)75.45±0.90 (4.6×10−03, 1)77.24±0.59 (2.4×10−05, 1)
    SDME58.99±1.41 (3.8×10−06, 1)66.84±1.34 (4.0×10−06, 1)72.71±1.09 (1.5×10−03, 1)76.05±0.55 (7.2×10−03, 1)78.30±0.30 (2.0×10−03, 1)
    LGSPE62.79±1.2570.34±1.0574.64±1.2577.04±0.8679.13±0.53
    下载: 导出CSV

    表  2  不同算法在Indian Pine数据集上各类地物的分类结果

    Table  2  Classification results of each class samples via different methods on Indian Pines dataset

    类别RAWPCALDALFDASNPESPPDLSPSDLDSPEMSMELGSFASDMELGSPE
    158.3366.6772.2261.1147.2263.8958.3358.3355.5652.7875.0077.7877.78
    255.6855.9367.1360.3862.3659.0658.9070.7666.9758.0777.3577.1879.98
    358.0158.1663.5559.8663.2658.0158.1664.4060.5759.4367.5268.6570.35
    438.3137.3151.7436.3240.8032.8443.2851.7439.8032.8452.2454.7361.19
    583.7084.1887.8384.1883.9483.9486.6288.5686.6282.0089.2990.5191.24
    693.2393.7196.7794.6893.7194.6894.5296.9495.1692.7497.5897.2697.90
    777.7877.7872.2255.5688.8955.5683.3383.3388.8966.6788.8988.8988.89
    894.8395.5799.0193.6094.8394.0996.0694.8395.5792.3696.0696.3196.80
    95060.00907070406010090601008070
    1066.5965.3861.7468.7769.6169.8565.9874.2167.4367.4374.3377.2477.60
    1170.8270.9673.5072.8375.1373.4172.2181.7474.7574.7080.8881.6082.89
    1243.2543.8570.6346.6353.7746.4349.8063.1062.3043.4569.6472.4275.99
    1389.0886.7897.1386.7887.9393.6885.6395.9894.8382.7694.8395.9895.98
    1488.9388.5694.0590.2390.8889.5890.3396.6591.5390.3394.6094.3395.26
    1535.3734.7659.1539.9438.1139.0235.0646.0442.9933.5450.9150.0056.71
    1692.4192.4188.6191.1489.8788.6192.4191.1486.0886.0886.0888.6189.87
    OA (%)69.6569.6576.1771.6273.3471.4371.2879.1274.6370.6580.5781.3283.02
    AA (%)68.5269.5077.8369.5071.8967.6770.6678.6174.9467.2080.9580.7281.78
    Kappa0.6540.6540.7280.6760.6950.6740.6730.7610.7100.6640.7780.7870.806
    DR 时间 (s)0.010.010.060.1321.9439.763.672.076.185.5236.6412.28
    下载: 导出CSV

    表  3  不同算法在PaviaU数据集上的分类结果(总体分类精度±标准差(%) ($p,h$))

    Table  3  Classification results with different methods on PaviaU dataset (OA ± Std (%) ($p,h$))

    算法0.4%0.8%1.2%1.6%2%
    RAW76.16±1.28 (1.8×10−09, 1)78.13±0.69 (2.4×10−15, 1)80.50±0.53 (5.2×10−17, 1)81.11±0.65 (4.7×10−16, 1)82.20±0.54 (3.0×10−17, 1)
    PCA76.15±1.27 (1.7×10−09, 1)78.12±0.69 (2.4×10−15, 1)80.49±0.54 (6.7×10−17, 1)81.10±0.64 (4.1×10−16, 1)82.18±0.54 (2.5×10−17, 1)
    LDA68.06±1.49 (1.2×10−14, 1)76.24±1.65 (1.1×10−12, 1)78.73±1.21 (4.2×10−14, 1)80.36±0.64 (7.0×10−17, 1)81.88±0.64 (1.1×10−16, 1)
    LFDA79.10±1.16 (3.8×10−06, 1)81.60±0.57 (1.0×10−11, 1)83.41±0.26 (6.6×10−15, 1)84.25±0.68 (1.7×10−11, 1)85.25±0.48 (3.7×10−13, 1)
    SNPE75.17±1.00 (4.5×10−11, 1)78.32±1.23 (1.2×10−12, 1)80.48±0.56 (9.0×10−17, 1)82.15±1.20 (1.9×10−11, 1)83.09±0.68 (7.7×10−15, 1)
    SPP67.23±0.44 (1.9×10−17, 1)73.63±1.27 (6.1×10−16, 1)76.24±0.96 (1.6×10−17, 1)78.13±0.48 (5.7×10−20, 1)79.19±0.92 (5.4×10−17, 1)
    DLSP74.32±1.80 (6.8×10−10, 1)77.73±1.10 (1.0×10−13, 1)80.47±0.80 (5.7×10−15, 1)79.69±1.39 (4.9×10−13, 1)81.52±1.02 (3.9×10−14, 1)
    SDL57.30±2.31 (9.4×10−17, 1)60.73±4.15 (2.2×10−13, 1)62.80±2.30 (1.4×10−17, 1)65.46±1.89 (2.2×10−18, 1)67.28±2.63 (1.3×10−15, 1)
    DSPE79.50±1.52 (6.0×10−05, 1)80.50±2.31 (6.6×10−07, 1)80.75±1.56 (1.8×10−10, 1)82.09±1.42 (1.7×10−10, 1)83.62±1.67 (2.1×10−08, 1)
    MSME75.24±1.76 (3.2×10−09, 1)79.32±1.90 (3.3×10−09, 1)82.94±1.01 (2.6×10−10, 1)83.94±1.12 (1.9×10−09, 1)85.41±0.64 (2.8×10−11, 1)
    LGSFA73.90±1.05 (4.7×10−12, 1)78.26±1.80 (1.6×10−10, 1)81.40±0.93 (5.3×10−13, 1)82.59±0.65 (2.7×10−14, 1)83.28±0.78 (9.5×10−14, 1)
    SDME79.67±1.84 (3.2×10−04, 1)82.32±0.74 (9.0×10−10, 1)83.57±0.68 (2.2×10−11, 1)84.67±0.78 (4.6×10−10, 1)85.35±0.60 (9.7×10−12, 1)
    LGSPE82.96±1.4686.24±0.7887.34±0.4788.23±0.5288.74±0.36
    下载: 导出CSV

    表  4  不同算法在PaviaU数据集上各类地物的分类结果

    Table  4  Classification results of each class samples via different methods on PaviaU dataset

    类别RAWPCALDALFDASNPESPPDLSPSDLDSPEMSMELGSFASDMELGSPE
    185.7685.7487.5986.2886.1783.6086.2566.8576.2885.3389.1785.2489.08
    293.4893.3393.7695.4994.6593.1993.2894.1993.5296.1497.2793.5097.92
    363.0962.9962.2469.2664.6957.3762.6949.0058.4864.5466.1063.2974.12
    483.1083.2484.4084.4084.9580.8783.3775.7879.6683.7290.4587.2988.94
    598.7598.7599.8499.3098.6799.2298.7599.6999.6199.7799.7799.6999.61
    663.0263.1668.8472.3568.0467.0662.8934.7273.7376.8158.0472.4882.52
    783.9383.6974.5186.2285.5976.8083.4554.8771.8182.3478.6284.4088.28
    880.7080.9376.6283.1681.6876.5380.8575.6170.1078.9075.0474.3083.59
    910010010010010099.6799.67879799.339998.89100
    OA (%)85.3785.3485.9088.1386.8884.3885.3576.6383.4088.0387.2986.2091.20
    AA (%)83.5083.5083.0486.2584.9081.5983.4770.9180.0785.2183.7984.3489.21
    Kappa0.8040.8040.8120.8410.8240.7910.8040.6820.7780.8400.8280.8160.883
    DR 时间 (s)0.0090.010.100.2823.7271.137.306.548.266.3133.1812.17
    下载: 导出CSV

    表  5  不同算法在 Indian Pines 和 PaviaU 数据集上的分类结果(总体分类精度±标准差(%) (Kappa))

    Table  5  Classification results with different methods on PaviaU and Indian Pines dataset (OA ± Std (%) (Kappa))

    510204060
    RAW44.02±3.74 (0.376)48.69±1.92 (0.430)55.25±2.36 (0.499)58.78±0.91 (0.538)61.10±0.33 (0.562)
    未滤波SSRSHE43.65±5.79 (0.375)49.16±5.34 (0.436)56.58±4.33 (0.515)59.91±2.70 (0.550)66.90±1.69 (0.626)
    IndianLGSPE45.58±2.97 (0.342)52.80±2.47 (0.470)61.82±2.88 (0.572)70.26±1.42 (0.664)73.29±1.09 (0.697)
    PinesRAW48.97±2.10 (0.432)58.53±1.92 (0.535)65.27±1.66 (0.609)70.14±1.65 (0.663)74.30±1.04 (0.709)
    滤波后SSRSHE65.04±4.15 (0.610)71.50±2.87 (0.680)79.30±1.63 (0.767)86.64±2.85 (0.848)87.75±1.15 (0.860)
    LGSPE69.17±2.72 (0.657)78.96±1.82 (0.763)85.48±1.48 (0.836)90.68±1.00 (0.894)93.92±0.50 (0.930)
    RAW56.85±7.57 (0.474)65.12±3.46 (0.564)68.82±2.72 (0.609)71.78±0.79 (0.644)74.58±0.59 (0.676)
    未滤波SSRSHE62.49±3.39 (0.538)64.29±1.73 (0.559)67.81±3.25 (0.599)71.21±1.71 (0.638)75.36±2.44 (0.685)
    PaviaULGSPE65.58±6.08 (0.567)69.91±6.06 (0.622)75.70±1.98 (0.690)81.76±1.71 (0.765)81.45±1.30 (0.760)
    RAW60.41±2.85 (0.514)67.39±2.46 (0.590)71.27±3.06 (0.639)76.11±1.14 (0.696)77.86±2.17 (0.718)
    滤波后SSRSHE71.18±4.85 (0.639)75.17±2.96 (0.686)82.86±2.07 (0.730)84.24±0.71 (0.795)87.02±0.97 (0.803)
    LGSPE76.10±3.53 (0.697)80.21±3.05 (0.700)86.70±2.18 (0.828)91.02±1.95 (0.883)93.72±0.93 (0.917)
    下载: 导出CSV
  • [1] 杜培军, 夏俊士, 薛朝辉, 谭琨, 苏红军, 鲍蕊. 高光谱遥感影像分类研究进展. 遥感学报, 2016, 20(2): 236-256.

    Du Pei-Jun, Xia Jun-Shi, Xue Zhao-Hui, Tan Kun, Su Hong-Jun, Bao Rui. Progress of hyperspectral remote sensing image classification. Journal of Remote Sensing, 2016, 20(2): 236-256.
    [2] 黄鸿, 李政英, 石光耀, 潘银松.面向高光谱影像分类的多特征流形鉴别嵌入.光学精密工程, 2019, 27(3): 726-738. doi: 10.3788/OPE.20192703.0726

    Huang Hong, Li Zheng-Ying, Shi Guang-Yao, Pan Ying-Song. Multi-features manifold discriminant embedding for hyperspectral image classification. Optics and Precision Engineering, 2019, 27(3):726-738. doi: 10.3788/OPE.20192703.0726
    [3] 张兵.高光谱图像处理与信息提取前沿.遥感学报, 2016, 20(5):1062-1090.

    Zhang Bing. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing. 2016, 20(5): 1062-1090.
    [4] 王天成, 刘相振, 董泽政, 王海波. 一种自适应鲁棒最小体积高光谱解混算法[J].自动化学报, 2017, 43(12): 2141-2159.

    Wang Tian-Cheng, Liu Xiang-Zhen, Dong Ze-Zheng, Wang Hai-Bo. A robust minimum volume based algorithm with automatically estimating regularization parameters for hy-perspectral unmixing. Acta Automatica Sinica, 2017, 43(12): 2141-2159.
    [5] Chen Mulin, Wang Qi, Li Xue-Long. Discriminant analysis with graph learning for hyperspectral image classification. Remote Sensing, 2018, 10(6):836. doi: 10.3390/rs10060836
    [6] 张号逵, 李映, 姜晔楠. 深度学习在高光谱图像分类领域的研究现状与展望. 自动化学报, 2018, 44(6): 961-977.

    Zhang Hao-Kui, Li Ying, Jiang Ye-Nan. Deep learning for hyperspectral imagery classification: the state of the art and prospects. Acta Automatica Sinica, 2018, 44(6): 961-977.
    [7] 张成坤, 韩敏.基于边缘保持滤波的高光谱影像光谱-空间联合分类[J].自动化学报, 2018, 44(2):280-288.

    Zhang Cheng-Kun, Han Min. Spectral-spatial joint classi-fication of hyperspectral image with edge-preserving filtering. Acta Automatica Sinica, 2018, 44(2): 280-288.
    [8] Gan Yu-Hang, Luo Fu-Lin, Liu Ju-Hua, Lei Bing, Zhang Tao, Liu Ke. Feature extraction based multi-structure manifold embedding for hyperspectral remote sensing image classification. IEEE Access, 2017, 5:25069-25080. doi: 10.1109/ACCESS.2017.2766242
    [9] Sun Wei-Wei, Yang Gang, Du Bo, Zhang Le-Fei, Zhang Liang-Pei. A sparse and low-rank near-isometric linear embedding method for reature extraction in hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7):4032-4046. doi: 10.1109/TGRS.2017.2686842
    [10] Karl P. On lines and planes of closest fit to systems of points in space. Philosophical Magazine. 1901, 2(11): 559-572.
    [11] Fisher R A. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 1936, 7: 179-188. doi: 10.1111/j.1469-1809.1936.tb02137.x
    [12] Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and data Representation. MIT Press, 2003.
    [13] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(5500): 2323-2326. doi: 10.1126/science.290.5500.2323
    [14] Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290(5500).
    [15] He X F, Cai D, Yan S C, Zhang H J. Neighborhood preserving embedding. In: Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV), Beijing, China, 2005, 2: 1208-1213.
    [16] He Xiao-Fei, Niyogi P. Locality preserving projections. Neural Information Processing Systems, 2004, 16: 153-160.
    [17] Yan Shui-Cheng, Xu Dong, Zhang Ben-Yu, Zhang Hong-Jiang, Yang Qiang, Lin S. Graph embedding and ex-tensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis & Machine Intelli-gence, 2007, 29(1):40-51.
    [18] Zeng Xian-Hua, Luo Si-Wei. A Supervised Subspace Learning Algorithm: Supervised neighborhood preserving embedding. Advanced Data Mining and Applications, Har-bin, China, 2007, 4632: 81-85
    [19] Zheng Zhong-Long, Yang Fan, Tan We-Nan, Jia Jiong, Yang Jie. Gabor feature-based face recognition using supervised locality preserving projection. Signal Processing, 2007, 87(10): 2473-2483. doi: 10.1016/j.sigpro.2007.03.006
    [20] Sugiyama M. Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. Journal of Machine Learning Research, 2007, 8(1): 1027-1061.
    [21] Luo Fu-Lin, Huang Hong, Duan Yu-Le, Liu Jia-Min, Liao Ying-Hua. Local geometric structure feature for dimen-sionality reduction of hyperspectral imagery. Remote Sensing, 2017, 9(8): 790-813. doi: 10.3390/rs9080790
    [22] Qiao Li-Shan; Chen Song-Can; Tan Xiao-Yang. Sparsity preserving projections with applications to face recognition. Pattern Recognition, 2010, 43(1):331-341. doi: 10.1016/j.patcog.2009.05.005
    [23] Zang Fei, Zhang Jiang-She. Discriminative learning by sparse representation for classification. Neurocomputing, 2011, 74:2176-2183. doi: 10.1016/j.neucom.2011.02.012
    [24] 罗甫林. 高光谱图像稀疏流形学习方法研究. 测绘学报, 2017, 46(3):400. doi: 10.11947/j.AGCS.2017.20160621

    Luo Fu-Lin. Sparse manifold learning for hyperspectral imagery. Acta Geodaetica et Cartographica Sinica, 2017, 46(3):400. doi: 10.11947/j.AGCS.2017.20160621
    [25] 罗甫林, 黄鸿, 刘嘉敏, 马泽忠.基于鉴别稀疏保持嵌入的高光谱影像地物分类.仪器仪表学报, 2016, 37(1): 177-183. doi: 10.3969/j.issn.0254-3087.2016.01.024

    Luo Fu-Lin, Huang Hong, Liu Jia-Min, Ma Ze-Zhong. Hyperspectral image land cover classification based on discriminant sparse preserving embedding. Chinese Journal of Scientific Instrument, 2016, 37(1):177-183. doi: 10.3969/j.issn.0254-3087.2016.01.024
    [26] Huang H, Li Z Y, He H B, Duan Y L, Yang S. Self-adaptive manifold discriminant analysis for feature extraction from hyperspectral imagery. Pattern Recognition, 2020, 107(9): 108487
    [27] Elhamifar E, Vidal R. Sparse manifold clustering and embedding. In: Proceedings of the 24th International Conference on Neural Information Processing and Systems. Granada, Spain, 2011. 55−63
    [28] Huang Hong, Luo Fu-Lin, Liu Jia-Ming, Yang Ya-Qiong. Dimensionality reduction of hyperspectral images based on sparse discriminant manifold embedding. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 106:42-54. doi: 10.1016/j.isprsjprs.2015.04.015
    [29] 黄鸿, 陈美利, 王丽华, 李政英.空-谱协同正则化稀疏超图嵌入的高光谱图像分类[J].测绘学报, 2019, 48(6):676- 687. doi: 10.11947/j.AGCS.2019.20180469

    Huang Hong, Chen Mei-Li, Wang Li-Hua, Li Zheng-Yin. Using Spatial-spectral Regularized Hypergraph Embedding for Hyperspectral Image Classification. Acta Geodaetica et Cartographica Sinica,, 2019, 48(6):676- 687. doi: 10.11947/j.AGCS.2019.20180469
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出版历程
  • 收稿日期:  2019-08-18
  • 录用日期:  2020-04-06
  • 网络出版日期:  2022-09-22
  • 刊出日期:  2022-10-14

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