Hyperspectral Image Classification Based on Three-Dimensional Dilated Convolutional Residual Neural Network

Mingjing Yan; Xiyou Su

doi:10.3788/AOS202040.1628002

[1] Meher S K. Semisupervised self-learning granular neural networks for remote sensing image classification[J]. Applied Soft Computing, 83, 105655(2019).

[2] Li Y F, Lin H. Multi-spectral remote sensing image classification of ground coverage based on CNN[J]. Microprocessors, 40, 43-48(2019).

[3] Liu P. Choo K K R, Wang L Z, et al. SVM or deep learning? A comparative study on remote sensing image classification[J]. Soft Computing, 21, 7053-7065(2017).

[4] Zhao C X, Qian L X. Comparative study of supervised and unsupervised classification in remote sensing image[J]. Journal of Henan University (Natural Science Edition), 34, 90-93(2004).

[5] Cao L L, Li H T, Han Y S et al. Application of convolutional neural networks in classification of high resolution remote sensing imagery[J]. Science of Surveying and Mapping, 41, 170-175(2016).

[6] Teng W X, Wang N, Chen T S et al. Deep adversarial domain adaptation method for cross-domain classification in high-resolution remote sensing images[J]. Laser & Optoelectronics Progress, 56, 112801(2019).

[7] Zhong Z L, Li J, Luo Z M et al. Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 847-858(2018).

[8] Mou L C, Ghamisi P, Zhu X X. Deep recurrent neural networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 3639-3655(2017).

[9] Dai X Y, Xue W. Hyperspectral remote sensing image classification based on convolutional neural network[C]∥37th Chinese Control Conference., 10373-10377(2018).

[10] Wolterink J M, Leiner T, Viergever M A et al[M]. Dilated convolutional neural networks for cardiovascular MR segmentation in congenital heart disease, 95-102(2017).

[11] Zhang H K, Li Y, Jiang Y N. Deep learning for hyperspectral imagery classification: the state of the art and prospects[J]. Acta Automatica Sinica, 44, 961-977(2018).

[12] Ji S W, Xu W, Yang M et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 221-231(2013).

[13] Feng B W, Lü X Q, Gu Y et al. Brain tumor segmentation using three-dimensional parallel CNN combined with dilated convolution[J]. Laser & Optoelectronics Progress, 57, 141009(2020).

[14] Sun Z J, Xue L, Xu Y M et al. Overview of deep learning[J]. Application Research of Computers, 29, 2806-2810(2012).

[15] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 27-30 June 2016, Las Vegas, NV, USA., 770-778(2016).

[16] He KM, Zhang XY, Ren SQ, et al.Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C]∥2015 IEEE International Conference on Computer Vision (ICCV). 7-13 Dec. 2015, Santiago, Chile. New York: IEEE Press, 2015: 1026- 1034.

[17] Ding J, Chen S T. Hyper-spectral remote sensing image classification based on residual 3D convolutional neural network[J]. Laser Journal, 40, 45-52(2019).

[18] He M Y, Li B, Chen H H. Multi-scale 3D deep convolutional neural network for hyperspectral image classification[C]∥2017 IEEE International Conference on Image Processing (ICIP). 17-20 Sept. 2017, Beijing, China., 3904-3908(2017).