Laser & Optoelectronics Progress, Volume. 57, Issue 12, 122803(2020)
Hyperspectral Image Classification Based on Dual-Channel Dilated Convolution Neural Network
[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, 1, 6-36(2013).
[2] van der Meer F. Analysis of spectral absorption features in hyperspectral imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 5, 55-68(2004).
[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, 18, 590-598(2007).
[7] Camps-Valls G, Bruzzone L. Kernel-based methods for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 43, 1351-1362(2005).
[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, 43, 492-501(2005).
[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, 42, 1335-1343(2004).
[10] Hughes G. On the mean accuracy of statistical pattern recognizers[J]. IEEE Transactions on Information Theory, 14, 55-63(1968).
[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, 46, 2112-2125(2008).
[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, 45, 4158-4171(2007).
[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, 4, 22-40(2016).
[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, 5, 8-36(2017).
[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, 16, 457-461(2019).
[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, 57, 5384-5394(2019).
[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, 16, 251-255(2019).
[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, 40, 834-848(2018).
[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, 6, 468-477(2015).
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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
Category: Remote Sensing and Sensors
Received: Oct. 1, 2019
Accepted: Oct. 29, 2019
Published Online: Jun. 3, 2020
The Author Email: Zhao Jingyi (zjylwsr@126.com)