Hyperspectral Images Classification Based on Multi-Feature Fusion and Hybrid Convolutional Neural Networks

Fan Feng; Shuangting Wang; Jin Zhang; Chunyang Wang

doi:10.3788/LOP202158.0810010

[1] Li S Y, Liu Z W, Liu K et al. Advances in application of space hyperspectral remote sensing(invited)[J]. Infrared and Laser Engineering, 48, 0303001(2019).

[2] Zhang J, Wei F Y, Feng F et al. Spatial-spectral feature refinement for hyperspectral image classification based on attention-dense 3D-2D-CNN[J]. Sensors, 20, 5191(2020). http://www.mdpi.com/1424-8220/20/18/5191/

[3] Pan B, Shi Z W, Xu X. MugNet: deep learning for hyperspectral image classification using limited samples[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 108-119(2018). http://www.sciencedirect.com/science/article/pii/S0924271617303416

[4] Zhou Y C, Wei Y T. Learning hierarchical spectral-spatial features for hyperspectral image classification[J]. IEEE Transactions on Cybernetics, 46, 1667-1678(2016). http://ieeexplore.ieee.org/document/7169539/references

[5] Bi X J, Zhou Z Y. Hyperspectral image classification algorithm based on two-channel generative adversarial network[J]. Acta Optica Sinica, 39, 1028002(2019).

[6] Liu B, Yu X C, Zhang P Q et al. Deep 3D convolutional network combined with spatial-spectral features for hyperspectral image classification[J]. Acta Geodaetica et Cartographica Sinica, 48, 53-63(2019).

[7] Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1798-1828(2013). http://newmed.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM23787338

[8] 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).

[9] Liu Y Z, Jiang Z Q, Ma F et al. Hyperspectral image classification based on hypergraph and convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 111007(2019).

[10] Li S T, Song W W, Fang L Y et al. Deep learning for hyperspectral image classification: an overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 6690-6709(2019). http://ieeexplore.ieee.org/document/8697135

[11] Makantasis K, Karantzalos K, Doulamis A et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C]. //2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 26-31, 2015, Milan, Italy., 4959-4962(2015).

[12] 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). http://www.tandfonline.com/doi/full/10.1080/2150704x.2015.1047045

[13] 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, 54, 6232-6251(2016). http://ieeexplore.ieee.org/document/7514991/citations?tabFilter=papers

[14] Li Y, Zhang H K, Shen Q. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network[J]. Remote Sensing, 9, 67(2017). http://www.ingentaconnect.com/content/doaj/20724292/2017/00000009/00000001/art00064

[15] Du P J, Xia J S, Xue Z H et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 20, 236-256(2016).

[16] Eldan R, Shamir O. The power of depth for feedforward neural networks[EB/OL]. [2020-07-25]. http://arxiv.org/abs/1512.03965

[17] 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), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[18] Lee H, Kwon H. Going deeper with contextual CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 26, 4843-4855(2017).

[19] 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).

[20] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2261-2269(2017).

[21] Wei X P, Yu X C, Tan X et al. Hyperspectral image classification based on residual dense network[J]. Laser & Optoelectronics Progress, 56, 151006(2019).

[22] Yan M J, Su X Y. Hyperspectral image classification based on three-dimensional dilated convolutional residual neural network[J]. Acta Optica Sinica, 40, 1628002(2020).

[23] Roy S K, Krishna G, Dubey S R et al. HybridSN:exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 17, 277-281(2020).

[24] Feng F, Wang S T, Wang C Y et al. Learning deep hierarchical spatial-spectral features for hyperspectral image classification based on residual 3D-2D CNN[J]. Sensors (Basel, Switzerland), 19, E5276(2019). http://www.ncbi.nlm.nih.gov/pubmed/31795511

[25] Chollet F. Xception:deep learning with depthwise separable convolutions[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 1800-1807(2017).

[26] Veit A, Wilber M, Belongie S. Residual networks behave like ensembles of relatively shallow networks[EB/OL]. [2020-07-23]. https://arxiv.org/abs/1605.06431