基于多特征和改进自编码的高光谱图像分类 下载: 1090次
张倩, 董安国, 宋睿. 基于多特征和改进自编码的高光谱图像分类[J]. 激光与光电子学进展, 2020, 57(8): 081010.
Qian Zhang, Anguo Dong, Rui Song. Hyperspectral Image Classification Based on Multiple Features and an Improved Autoencoder[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081010.
[1] 刘大伟, 韩玲, 韩晓勇. 基于深度学习的高分辨率遥感影像分类研究[J]. 光学学报, 2016, 36(4): 0428001.
[2] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[4] Camps-Valls G, Bruzzone L. Kernel-based methods for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(6): 1351-1362.
[5] Gao L R, Li J, Khodadadzadeh M, et al. Subspace-based support vector machines for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(2): 349-353.
[6] Xing C, Ma L, Yang X Q. Stacked denoise autoencoder based feature extraction and classification for hyperspectral images[J]. Journal of Sensors, 2016, 2016: 3632943.
[7] Chen Y S, Zhao X, Jia X P. Spectral-spatial classification of hyperspectral data based on deep belief network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2381-2392.
[8] CaoJ, ChenZ, WangB. Deep convolutional networks with superpixel segmentation for hyperspectral image classification[C]//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 10-15, 2016, Beijing, China. New York: IEEE, 2016: 3310- 3313.
[10] Chen Y S, Jiang H L, Li C Y, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6232-6251.
[11] Dey N, Hong S, Ach T, et al. Tensor decomposition of hyperspectral images to study autofluorescence in age-related macular degeneration[J]. Medical Image Analysis, 2019, 56: 96-109.
[12] Ahmadi S A, Mehrshad N, Razavi S M. Noise reduction and feature extraction based on low-rank representation and pairwise constraint preserving for hyperspectral images[J]. International Journal of Remote Sensing, 2019, 40(22): 8236-8269.
[13] Rahmani V, Rostami V. Adaptive color mapping for NAO robot using neural network[J]. Advances in Computer Science: an International Journal, 2014, 3(5): 66-71.
[15] 叶珍, 白璘. 基于主成分分析与局部二值模式的高光谱图像分类[J]. 激光与光电子学进展, 2017, 54(11): 111006.
[16] 于纯妍, 赵猛, 宋梅萍, 等. 基于目标约束与谱空迭代的高光谱图像分类方法[J]. 光学学报, 2018, 38(6): 0628003.
[17] 黄鸿, 何凯, 郑新磊, 等. 基于深度学习的高光谱图像空-谱联合特征提取[J]. 激光与光电子学进展, 2017, 54(10): 101001.
张倩, 董安国, 宋睿. 基于多特征和改进自编码的高光谱图像分类[J]. 激光与光电子学进展, 2020, 57(8): 081010. Qian Zhang, Anguo Dong, Rui Song. Hyperspectral Image Classification Based on Multiple Features and an Improved Autoencoder[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081010.