[1] 陈雪, 马建文, 戴芹. 基于贝叶斯网络分类的遥感影像变化检测[J]. 遥感学报, 2005, 9(6): 667-672.

    Chen X, Ma J W, Dai Q. Remote sensing change detection based on Bayesian networks classifications[J]. Journal of Remote Sensing, 2005, 9(6): 667-672.

[2] Sukawattanavijit C, Chen J, Zhang H S. GA-SVM algorithm for improving land-cover classification using SAR and optical remote sensing data[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(3): 284-288.

[3] Frenay B, Verleysen M. Classification in the presence of label noise: a survey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(5): 845-869.

[4] Pelletier C, Valero S, Inglada J, et al. Effect of training class label noise on classification performances for land cover mapping with satellite image time series[J]. Remote Sensing, 2017, 9(2): 173.

[5] 杨斌, 王翔. 基于深度残差去噪网络的遥感融合图像质量提升[J]. 激光与光电子学进展, 2019, 56(16): 161009.

    Yang B, Wang X. Boosting quality of pansharpened images using deep residual denoising network[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161009.

[6] AngelovaA, Abu-MostafamY, PeronaP. Pruning training sets for learning of object categories[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE, 2005: 494- 501.

[7] Brodley C E, Friedl M A. Identifying mislabeled training data[J]. Journal of Artificial Intelligence Research, 1999, 11: 131-167.

[8] BüschenfeldT, Ostermann J. Automatic refinement of training data for classification of satellite imagery[J].ISPRS Annals of Photogrammetry, RemoteSensing and Spatial InformationSciences, 2012, I-7: 117- 122.

[9] Chellasamy M. Ferré T P A, Greve M H. An ensemble-based training data refinement for automatic crop discrimination using WorldView-2 imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(10): 4882-4894.

[10] Rousseeuw P J, Hubert M. Robust statistics for outlier detection[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2011, 1(1): 73-79.

[11] Leys C, Ley C, Klein O, et al. detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median[J]. Journal of Experimental Social Psychology, 2013, 49(4): 764-766.

[12] Gong X Q, Shen L, Lu T D. Refining training samples using median absolute deviation for supervised classification of remote sensing images[J]. Journal of the Indian Society of Remote Sensing, 2019, 47(4): 647-659.

[13] Koda S, Zeggada A, Melgani F, et al. Spatial and structured SVM for multilabel image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 5948-5960.

[14] Hampel F R. The influence curve and its role in robust estimation[J]. Journal of the American Statistical Association, 1974, 69(346): 383-393.

[15] Huber PJ. Robust statistics[M] //Lovric M. International Encyclopedia of Statistical Science. Berlin: Springer, 2011: 1248- 1251.

[16] 裴欢, 孙天娇, 王晓妍. 基于Landsat 8 OLI影像纹理特征的面向对象土地利用/覆盖分类[J]. 农业工程学报, 2018, 34(2): 248-255.

    Pei H, Sun T J, Wang X Y. Object-oriented land use/cover classification based on texture features of Landsat 8 OLI image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(2): 248-255.

[17] 王书涛, 吴兴, 朱文浩, 等. 平行因子结合支持向量机对多环芳烃的荧光检测[J]. 光学学报, 2019, 39(5): 0530002.

    Wang S T, Wu X, Zhu W H, et al. Fluorescence detection of polycyclic aromatic hydrocarbons by parallel factor combined with support vector machine[J]. Acta Optica Sinica, 2019, 39(5): 0530002.

[18] Foody G M, Mathur A. The use of small training sets containing mixed pixels for accurate hard image classification: training on mixed spectral responses for classification by a SVM[J]. Remote Sensing of Environment, 2006, 103(2): 179-189.

[19] 王民, 樊潭飞, 贠卫国, 等. PFWG改进的CNN多光谱遥感图像分类[J]. 激光与光电子学进展, 2019, 56(3): 031003.

    Wang M, Fan T F, Yun W G, et al. PFWG improved CNN multispectral remote sensing image classification[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031003.

[20] Liu C R, Frazier P, Kumar L. Comparative assessment of the measures of thematic classification accuracy[J]. Remote Sensing of Environment, 2007, 107(4): 606-616.

[21] 吴波, 林珊珊, 周桂军. 面向对象的高分辨率遥感影像分割分类评价指标[J]. 地球信息科学学报, 2013, 15(4): 567-573.

    Wu B, Lin S S, Zhou G J. Quantitatively evaluating indexes for object-based segmentation of high spatial resolution image[J]. Journal of Geo-Information Science, 2013, 15(4): 567-573.

[22] 杨永可, 肖鹏峰, 冯学智, 等. 大尺度土地覆盖数据集在中国及周边区域的精度评价[J]. 遥感学报, 2014, 18(2): 453-475.

    Yang Y K, Xiao P F, Feng X Z, et al. Comparison and assessment of large-scale land cover datasets in China and adjacent regions[J]. Journal of Remote Sensing, 2014, 18(2): 453-475.