针对高光谱端元提取的空谱联合预处理方法
[1] WU Y H, HU B L, GAO X H, et al. Hyperspectral image classification based on adaptive segmentation[J]. Optik, 2018, 172: 612-621.
[2] 成宝芝, 张丽丽.基于双边滤波的最优波段子空间高光谱异常目标检测[J].液晶与显示, 2019, 34(9): 897-904.
[3] 黄元超, 王阿川.基于空谱联合和波段分类的高光谱压缩感知重构[J].液晶与显示, 2018, 33(4): 291-298.
[4] 李冠东,张春菊,高飞,等. 双卷积池化结构的3D-CNN高光谱遥感影像分类方法[J]. 中国图象图形学报, 2019,24(4): 639-654.
LI G D, ZHANG C J, GAO F, et al, Doubleconvpool-structured 3D-CNN for hyperspectral remote sensing image classification[J]. Journal of Image and Graphics, 2019, 24(4): 639-654. (in Chinese)
[5] 谭翠媚, 许廷发, 马旭,等. 图-谱结合的压缩感知高光谱视频图像复原[J]. 中国光学, 2018, 11(6): 949-957.
[6] 闫歌, 许廷发, 马旭,等. 动态测量的高光谱图像压缩感知[J]. 中国光学, 2018, 11(4): 550-559.
[7] 杜小平, 刘明, 夏鲁瑞,等. 基于光谱角累加的高光谱图像异常检测算法[J]. 中国光学, 2013, 6(3): 325-331.
[8] 方帅,祝凤娟,董张玉, 等. 样本优化选择的高光谱图像分类[J]. 中国图象图形学报, 2019,24(1): 135-148.
FANG S, ZHU F J, DONG Z Y, et al. Sample optimized selection of hyperspectral image classification[J]. Journal of Image and Graphics, 2019, 24(1): 135-148. (in Chinese)
[9] 冉琼,于浩洋,高连如, 等. 结合超像元和子空间投影支持向量机的高光谱图像分类[J]. 中国图象图形学报, 2018,23(1): 95-105.
RAN Q, YU H Y, GAO LI R, et al. Superpixel and subspace projection-based support vector machines for hyperspectral image classification[J]. Journal of Image and Graphics, 2018, 23(1): 95-105. (in Chinese)
[10] WU K, FENG X X, XU H G, et al. A novel endmember extraction method using sparse component analysis for hyperspectral remote sensing imagery[J]. IEEE Access, 2018, 6: 75206-75215.
[11] SONG M P, XU M, CHANG C I. Algorithm research on endmember extraction combined with distribution statistics[C]//Proceedings of the 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Amsterdam, Netherlands: IEEE, 2018: 1-4.
[12] KOWKABI F, KESHAVARZ A. Hyperspectral endmember extraction preprocessing using combination of Euclidean and geodesic distances[C]//Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: IEEE, 2018: 4265-4268.
[13] NASCIMENTO J M P, DIAS J M B. Vertex component analysis: a fast algorithm to unmix hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.
[14] GRUNINGERJ H, RATKOWSKI A J, HOKE M L. The sequential maximum angle convex cone (SMACC) endmember model[C]//Proceedings of SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X. Orlando, USA: SPIE, 2004.
[15] MEI S H, HE M Y, WANG Z Y, et al. Spatial purity based endmember extraction for spectral mixture analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(9): 3434-3445.
[16] 崔建涛, 王晶, 厉小润, 等.基于空间像素纯度指数的端元提取算法[J].浙江大学学报: 工学版, 2013, 47(9): 1524-1530, 1565.
[17] HEINZ D, CHANG C I, ALTHOUSE M L G. Fully constrained least-squares based linear unmixing[hyperspectral image classification][C]//Proceedings of IEEE 1999 International Geoscience and Remote Sensing Symposium. Hamburg, Germany: IEEE, 1999: 1401-1403.
吴银花, 王鹏冲, 吴慎将, 张发强. 针对高光谱端元提取的空谱联合预处理方法[J]. 液晶与显示, 2020, 35(9): 955. WU Yin-hua, WANG Peng-chong, WU Shen-jiang, ZHANG Fa-qiang. Spatial-spectral combined preprocessing method for hyperspectral endmember extraction[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(9): 955.