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Application of Hyperspectral Image Classification Based on Overlap Pooling

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Abstract

Convolutional neural networks (CNN) are increasingly being used in hyperspectral image (HSI) classification. However, most pooling methods are non-overlap pooling and ignore the influence of neighboring pixels on image characteristics, thereby limiting network classification accuracy. This work presents a deep CNN that is based on overlap pooling; in this model, non-overlap pooling is replaced with overlap pooling to improve the accuracy of feature extraction. However, overlap pooling introduces additional noise while improving feature accuracy. We have found that different combinations of max pooling and mean pooling can effectively solve the problem and significantly improve classification performance. The best pooling combination (max–mean–mean) for HSI classification is obtained after verification through experiments. A rectified linear unit activation function layer and the softmax loss classification model are combined to improve overall classification accuracy. Experiments on three HSI data sets, namely, Indian Pines, Salinas and Pavia University, show that the CNN model can increase overall accuracy to 95.66, 97.8 and 97.48%, respectively. Compared with deep network models such as deep belief network and non-overlap CNN, the proposed model has significantly improved the classification accuracy, and thus verifying the high accuracy of feature extraction of overlap pooling in CNN.

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References

  1. Benediktsson JA, Ghamisi P (2015) Spectral–spatial classification of hyperspectral remote sensing images. Artech House, Boston

    Google Scholar 

  2. Du Q, Zhang L, Zhang B, Tong X, Du P, Chanussot J (2013) Foreword to the special issue on hyperspectral remote sensing: theory, methods, and applications. IEEE J Sel Top Appl Earth Observ Remote Sens 6(2):459–465

    Article  Google Scholar 

  3. Younan N, Aksoy S, King R (2012) Foreword to the special issue on pattern recognition in remote sensing. IEEE J Sel Top Appl Earth Observ Remote Sens 5(5):1331–1334

    Article  Google Scholar 

  4. Chang C-I (2013) Hyperspectral data processing: algorithm design and analysis. Wiley, New York

    Book  MATH  Google Scholar 

  5. Blanzieri E, Melgani F (2008) Nearest neighbor classification of remote sensing images with the maximal margin principle. IEEE Trans Geosci Remote Sens 46(6):1804–1811

    Article  Google Scholar 

  6. Gao L et al (2015) Subspace-based support vector machines for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12(2):349–353

    Article  Google Scholar 

  7. Ma X, Geng J, Wang H (2015) Hyperspectral image classification via contextual deep learning. EURASIP J Image Video Process 20(1):1–12

    Google Scholar 

  8. Zabalza J et al (2016) Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185(12):1–10

    Article  Google Scholar 

  9. Ma X, Wang H, Geng J (2016) Spectral–spatial classification of hyperspectral image based on deep auto-encoder. IEEE J Sel Top Appl Earth Obs Remote Sens 99:1–13

    Google Scholar 

  10. Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2094–2107

    Article  Google Scholar 

  11. Chen Y, Zhao X, Jia X (2015) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):2381–2392

    Article  Google Scholar 

  12. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  13. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), pp 1097–1105

  14. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: The IEEE conference on computer vision and pattern recognition (CVPR)

  15. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp 248–255

  16. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: The IEEE conference on computer vision and pattern recognition (CVPR)

  17. Zhao W, Du S (2016) Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS J Photogramm Remote Sens 113:155–165

    Article  Google Scholar 

  18. Zhao W, Du S (2016) Spectral–spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans Geosci Remote Sens 54(8):4544–4554

    Article  Google Scholar 

  19. Romero A, Gatta C, Camps-Valls G (2016) Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(3):1349–1362

    Article  Google Scholar 

  20. Makantasis K, Karantzalos K, Doulamis A, Doulamis N (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: Proceedings of IGARSS, pp 4959–4962

  21. Li W, Wu G, Zhang F et al (2016) Hyperspectral image classification using deep pixel-pair features. IEEE Trans Geosci Remote Sens 55(2):844–853

    Article  Google Scholar 

  22. Chen Y, Jiang H, Li C et al (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251

    Article  Google Scholar 

  23. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  24. Qian Y, Ye M, Zhou J (2012) Hyperspectral image classification based structured sparse logistic regression and three-dimensional wavelet texturefeatures. IEEE Trans Geosci Remote Sens 51(4):2276–2291

    Article  Google Scholar 

  25. Shen L, Jia S (2011) Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 49(12):5039–5046

    Article  Google Scholar 

  26. Tang YY, Lu Y, Yuan H (2015) Hyperspectral image classification based on three-dimensional scattering wavelet transform. IEEE Trans Geosci Remote Sens 53(5):2467–2480

    Article  Google Scholar 

  27. Li W et al (2015) Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 53(7):3681–3693

    Article  Google Scholar 

  28. Waske B, van der Linden S, Benediktsson JA, Rabe A, Hostert P (2010) Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Trans Geosci Remote Sens 48(7):2880–2889

    Article  Google Scholar 

  29. Hu W, Huang Y, Wei L, Zhang F, Li H (2015) Deep convolutional neural networks for hyperspectral image classification. J Sens. 2015, Art. no. 258619. https://doi.org/10.1155/2015/258619

  30. Li W, Wu G, Zhang F, Du Q (2017) Hyperspectral image classification using deep pixel-pair features. IEEE Trans Geosci Remote Sens 55(2):844–853

    Article  Google Scholar 

  31. Santara A et al (2016) BASS net: band-adaptive spectral–spatial feature learning neural network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(9):5293–5301

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61701166), China Postdoctoral Science Foundation (2018M632215), Fundamental Research Funds for the Central Universities (2018B16314), Projects in the National Science and Technology Pillar Program during the Twelfth Five-year Plan Period (2015BAB07B01).

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  1. Hongmin Gao and Shuo Lin have contributed equally to this work and should be considered co-first author.

Authors and Affiliations

  1. College of Computer and Information Engineering, Hohai University, Nanjing, 211100, China

    Hongmin Gao, Shuo Lin, Chenming Li & Yao Yang

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  1. Hongmin Gao
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  2. Shuo Lin
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  3. Chenming Li
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  4. Yao Yang
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Correspondence to Chenming Li.

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Gao, H., Lin, S., Li, C. et al. Application of Hyperspectral Image Classification Based on Overlap Pooling. Neural Process Lett 49, 1335–1354 (2019). https://doi.org/10.1007/s11063-018-9876-7

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