[1] [1] ZHANG L, WEI W, ZHANG Y N, et al. Cluster sparsity field: An internal hyperspectral imagery prior for reconstruction［J］. International Journal of Computer Vision, 2018, 126(8): 797-821.

[2] [2] LI Sh T, HAO Q N, GAO G H, et al. The effect of ground truth on performance evaluation of hyperspectral image classification［J］. IEEE Transactions on Geoence & Remote Sensing, 2018, 56(12): 7195-7206.

[3] [3] PARK B, LU R F. Hyperspectral imaging technology in food and agriculture［M］. New York,USA: Springer, 2015: 305-331.

[4] [4] RUITENBEEK F, DEBBA P, MEER F D, et al. Mapping white micas and their absorption wavelengths using hyperspectral band ratios［J］. Remote Sensing of Environment, 2006, 102(3/4):211-222.

[5] [5] HU Y F, ZHANG Q L, ZHANG Y Z, et al. A deep convolution neural network method for land cover mapping: A case study of Qinhuangdao, China［J］. Remote Sensing, 2018, 10(12):2053.

[6] [6] GUAN Sh H, YANG G, LI H, et al. Hyperspectral image classification based on 3-D convolutional recurrent neural network［J］. Laser Technology, 2020, 44(4): 485-491(in Chinese).

[7] [7] ZHANG X G, GAO Z Y, JIAO L C, et al. Multifeature hyperspectral image classification with local and nonlocal spatial information via markov random field in semantic space［J］. IEEE Transactions on Geoence & Remote Sensing, 2018, 56(3): 1409-1424.

[8] [8] FANG L Y, HE N J, LI Sh T, et al. A new spatial-spectral feature extraction method for hyperspectral images using local covariance matrix representation［J］. IEEE Transactions on Geoence & Remote Sensing, 2018, 56(6): 3534-3546.

[9] [9] LU Zh W, FU Zh Y, XIANG T, et al. Learning from weak and noisy labels for semantic segmentation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(3): 486-500.

[10] [10] FOODY G M. The effect of mis-labeled training data on the accuracy of supervised image classification by SVM［C］// 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). New York, USA: IEEE, 2015: 4987-4990.

[11] [11] KANG X D, DUAN P H, XIANG X L, et al. Detection and correction of mislabeled training samples for hyperspectral image classification［J］. IEEE Transactions on Geoence & Remote Sensing, 2018, 56(10): 5673-5686.

[12] [12] TU B, ZHOU Ch L, KUANG W L, et al. Hyperspectral imagery noisy label detection by spectral angle local outlier factor［J］. IEEE Geoscience and Remote Sensing Letters, 2018, 15(9): 1417-1421.

[13] [13] ALEX R, LAIO A. Clustering by fast search and find of density peaks［J］. Science, 2014, 344(6191):1492-1496.

[14] [14] TU B, ZHANG X F, KANG X D, et al. Density peak-based noisy label detection for hyperspectral image classification［J］. IEEE Transactions on Geoence & Remote Sensing, 2019, 57(3): 1573-1584.

[15] [15] TU B, ZHANG X F, KANG X D, et al. Spatial density peak clustering for hyperspectral image classification with noisy labels［J］. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7): 5085-5097.

[16] [16] TU B, ZHANG Ch L, PENG J, et al. Kernel entropy component analysis-based robust hyperspectral image supervised classification［J］. Remote Sensing, 2019, 11(23): 2823.

[17] [17] ZOU Zh X, SHI Zh W. Quadratic constrained energy minimization for hyperspectral target detection［C］//2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). New York, USA: IEEE, 2015: 4979-4982.

[18] [18] ZHANG Y F, XIE B B, SUN J, et al. A hybrid sparsity and constrained energy minimization detector for hyperspectral images［C］//2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). New York, USA: IEEE, 2017: 1137-1140.

[19] [19] TU B, ZHOU Ch L, LIAO X L, et al. Hierarchical structure-based noisy labels detection for hyperspectral image classification［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 2183-2199.

[20] [20] KIM S J, KOH K, LUSTIG M, et al. An interior-point method for large-scale & 1-regularized least squares［J］. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 606-617.

[21] [21] BIOUCAS-DIAS J M, NASCIMENTO J M. Hyperspectral subspace identification［J］. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(8): 2435-2445.

[22] [22] KRUSE F A, LEFKOFF A B, BOARDMAN J W, et al. The spectral image processing system (SIPS): Software for integrated analysis of AVIRIS data［J］. Remote Sensing of Environment, 1993, 44(2/3): 145-163.

[23] [23] CUI M S, PRASAD S. Class-dependent sparse representation classifier for robust hyperspectral image classification［J］. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(5): 2683-2695.

[24] [24] SU H J, SHENG Y H. Orthogonal projection divergence-based hyperspectral band selection［J］. Spectroscopy and Spectral Analysis, 2011, 31(5): 1309-1313(in Chinese).

[25] [25] TU B, YANG X C, LI N Y, et al. Hyperspectral image classification via superpixel correlation coefficient representation［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(11): 4113-4127.

[26] [26] MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines［J］. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1778-1790.