High spatial resolution remote sensing image classification based on deep learning

Guangxue Xuebao/Acta Optica Sinica

Volumn 36, Issue 4, 2016, Pages

  Liu, Dawei ^a,b   Han, Ling ^a   Han, Xiaoyong ^a  

^a CHANG'AN UNIVERSITY   (China)

^b Armed Police Engineering University   (China)

Author keywords

Deep belief networks; Deep learning; High spatial resolution; Nonsubsampled contourlet transform; Remote sensing; Remote sensing image classification; Texture

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE ENHANCEMENT; IMAGE RECONSTRUCTION; IMAGE RESOLUTION; IMAGE TEXTURE; INFORMATION USE; REMOTE SENSING; SUPPORT VECTOR MACHINES; TEXTURES;

DEEP BELIEF NETWORKS; DEEP LEARNING; HIGH SPATIAL RESOLUTION; NON-SUB-SAMPLED CONTOURLET TRANSFORMS; REMOTE SENSING IMAGE CLASSIFICATION;

IMAGE CLASSIFICATION;

EID: 84964306850     PISSN: 02532239     EISSN: None     Source Type: Journal    
DOI: 10.3788/AOS201636.0428001     Document Type: Article

References (25)

1. Luo B, Zhang L. Robust autodual morphological profiles for the classification of high-resolution satellite images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1451-1462.
2. Pacifici F, Chini M, Emery W J. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification[J]. Remote Sensing of Environment, 2009, 113(6): 1276-1292.
3. Huang X, Zhang L. An SVM ensemble approach combining spectral, structural, and semantic features for the classification of highresolution remotely sensed imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1): 257-272.
4. Liu C, Hong L, Chen J, et al. Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image[J]. Journal of Remote Sensing, 2015, 19(2): 228-239.
5. Bengio Y. Learning deep architectures for AI[J]. Foundations and Trends in Machine Learning, 2009, 2(1): 1-127.
6. Bottou L. Large-scale kjernel machines[M]. Cambridge: MIT Press, 2007: 321-359.
7. Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
8. Yu D, Deng L. Deep learning and its applications to signal and information processing[J]. IEEE Signal Processing Magazine, 2011, 28(1): 145-154.
9. Jones N. The learning machines[J]. Nature, 2014, 505(7842): 146-148.
10. Yu Kai, Jia Lei, Chen Yuqiang, et al. Deep learning: Yesterday, today and tomorrow[J]. Journal of Computer Research and Development, 2013, 50(9): 1799-1804.
11. Hinton G. Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002, 14(8): 1771-1800.
12. Jin Jing, Zou Zhengrong, Tao Chao. Compressed texton based high resolution remote sensing image classification[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(5): 493-499.
13. Do M N, Vetterli M. The Contourlet transform: An efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.
14. Cunha A L, Zhou J, Do M N. The nonsubsampled contourlet transform: Theory, design, and applications[J]. Image Processing, IEEE Transactions on, 2006, 15(10): 3089-3101.
15. Zhou J, Cunha A L, Do M N. Nonsubsampled contourlet transform: Construction and application in enhancement[C]. Image Processing, 2005. ICIP 2005. IEEE International Conference on, IEEE, 2005, 1: 469-472.
16. Wu Xiaoyue, Guo Baolong, Tang Lu, et al. A new adaptive image denoising method based on the nonsubsampled Contourlet transform algorithm[J]. Acta Optica Sinica, 2009, 29(8): 2147-2152.
17. Zhou Jinghong, Zhou Cui, Zhu Jianjun, et al. A method of super-resolution reconstruction for remote sensing image based on nonsubsampled Contourlet transform[J]. Acta Optica Sinica, 2015, 35(1): 0110001.
18. He Yongcong, Liu Wenbo, Zhang Gong, et al. Rotation-invariant texture image retrieval algorithm based on non-subsampled Contourlet transform[J]. Journal of Image and Graphics, 2011, 16(1): 79-83.
19. Wu Yiquan, Yin Jun, Zhu Li, et al. Method for improving reconstructed image quality of digital hologram based on SRAD and NSCT[J]. Chinese J Lasers, 2014, 41(2): 0209024.
20. Zhou Liangji, Li Qingwu, Huo Guanying, et al. Fase image recognition method based on the NSCT and bionic pattern[J]. Laser & Optoelectronics Progress, 2015, 52(3): 031001.
21. Chen Musheng, Cai Zhishan. Study on fusion of visual and infrared images based on NSCT[J]. Laser & Optoelectronics Progress, 2015, 52(6): 061002.
22. Huang Xin. Multiscale texture and shape feature extraction and object-oriented classification for very high resolution remotely sensed imagery[D]. Wuhan: Wuhan University, 2009: 71-76.
23. Hinton G. A practical guide to training restricted Boltzmann machines[J]. Momentum, 2010, 9(1): 926.
24. Roux N L, Bengio Y. Representational power of restricted Boltzmann machines and deep belief networks[J]. Neural Computation, 2008, 20(6): 1631-1649.
25. Larochelle H, Bengio Y, Louradour J, et al. Exploring strategies for training deep neural networks[J]. Journal of Machine Learning Research, 2009, 10(1): 1-40.