基于双通道空洞卷积神经网络的高光谱图像分类 下载: 1078次
胡丽, 单锐, 王芳, 江国乾, 赵静一, 张智. 基于双通道空洞卷积神经网络的高光谱图像分类[J]. 激光与光电子学进展, 2020, 57(12): 122803.
Li Hu, Rui Shan, Fang Wang, Guoqian Jiang, Jingyi Zhao, Zhi Zhang. Hyperspectral Image Classification Based on Dual-Channel Dilated Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(12): 122803.
[1] Bioucas-Dias J M, Plaza A, Camps-Valls G, et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(2): 6-36.
[2] van der Meer F. Analysis of spectral absorption features in hyperspectral imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2004, 5(1): 55-68.
[3] Gowen A, Odonnell C, Cullen P, et al. Hyperspectral imaging-an emerging process analytical tool for food quality and safety control[J]. Trends in Food Science & Technology, 2007, 18(12): 590-598.
[7] 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.
[8] Ham J, Chen Y C, Crawford M M, et al. Investigation of the random forest framework for classification of hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 492-501.
[9] Foody G M, Mathur A. A relative evaluation of multiclass image classification by support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(6): 1335-1343.
[10] Hughes G. On the mean accuracy of statistical pattern recognizers[J]. IEEE Transactions on Information Theory, 1968, 14(1): 55-63.
[11] Samaniego L, Bardossy A, Schulz K. Supervised classification of remotely sensed imagery using a modified K-NN technique[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(7): 2112-2125.
[12] Martínez-Usómartinez-uso A, Pla F, Sotoca J M, et al. Clustering-based hyperspectral band selection using information measures[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(12): 4158-4171.
[13] Zhang L P, Zhang L F, Du B. Deep learning for remote sensing data: a technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2): 22-40.
[14] Zhu X X, Tuia D, Mou L C, et al. Deep learning in remote sensing: a comprehensive review and list of resources[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(4): 8-36.
[15] Ouyang N, Zhu T, Lin L P. Convolutional neural network trained by joint loss for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(3): 457-461.
[16] 毕晓君, 周泽宇. 基于双通道GAN的高光谱图像分类算法[J]. 光学学报, 2019, 39(10): 1028002.
[19] Hang R L, Liu Q S, Hong D F, et al. Cascaded recurrent neural networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8): 5384-5394.
[20] Niu Z J, Liu W, Zhao J Y, et al. DeepLab-based spatial feature extraction for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(2): 251-255.
[22] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.
[24] Yue J, Zhao W Z, Mao S J, et al. Spectral-spatial classification of hyperspectral images using deep convolutional neural networks[J]. Remote Sensing Letters, 2015, 6(6): 468-477.
胡丽, 单锐, 王芳, 江国乾, 赵静一, 张智. 基于双通道空洞卷积神经网络的高光谱图像分类[J]. 激光与光电子学进展, 2020, 57(12): 122803. Li Hu, Rui Shan, Fang Wang, Guoqian Jiang, Jingyi Zhao, Zhi Zhang. Hyperspectral Image Classification Based on Dual-Channel Dilated Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(12): 122803.