Skip to main content
SpringerLink
Log in

Multi-feature Fusion Based Deep Forest for Hyperspectral Image Classification

  • Conference paper
  • First Online:
Bio-Inspired Computing: Theories and Applications (BIC-TA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1566))

  • 584 Accesses

Abstract

Multi-feature fusion is a useful way to improve the classification of hyperspectral image (HSI). But the multi-feature fusion is usually at the decision level of classifier, which causes less link between features or poor extensibility of feature. In this paper, we propose a multi-feature fusion based deep forest method for HSI classification, named mfdForest. In mfdForest, the morphological features, saliency features, and edge features are extracted, then the three deep multi-grained scanning branches in dgcForest (one of improved deep forest) are used to extract and fuse the extracted features deeply, and the fused features are sent into cascade forest in dgcForest for classification. Experimental results indicate that the proposed framework consumes less training time and has better performance on two HSI data sets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tang, Y., et al.: Apple bruise grading using piecewise nonlinear curve fitting for hyperspectral imaging data. IEEE Access 8, 147494–147506 (2020)

    Article  Google Scholar 

  2. Shimoni, M., Haelterman, R., Perneel, C.: Hypersectral imaging for military and security applications: combining myriad processing and sensing techniques. IEEE Geosci. Remote Sens. Mag. 7(2), 101–117 (2019)

    Article  Google Scholar 

  3. Pike, R., Lu, G., Wang, D., Chen, Z.G., Fei, B.: A Minimum spanning forest-based method for noninvasive cancer detection with hyperspectral imaging. IEEE Trans. Biomed. Eng. 63(3), 653–663 (2016)

    Article  Google Scholar 

  4. Zhang, L., Zhang, L., Du, B.: Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosci. Remote Sens. 4(2), 22–40 (2016)

    Article  Google Scholar 

  5. Yang, C., Li, Y., Peng, B., Cheng Y., Tong, L.: Road material information extraction based on multi-feature fusion of remote sensing image. In: 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). pp. 3943–3946, IEEE (2019)

    Google Scholar 

  6. Lu, J., Ma, C., Zhou, Y., Luo, M., Zhang, K.: Multi-feature fusion for enhancing image similarity learning. IEEE Access 7, 167547–167556 (2019)

    Article  Google Scholar 

  7. Zhao, S., Nie, W., Zhang, B.: Multi-feature fusion using collaborative residual for hyperspectral palmprint recognition. In: 2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China, pp. 1402–1406, IEEE (2018)

    Google Scholar 

  8. He, M., Li, B., Chen, H.: Multi-scale 3D deep convolutional neural network for hyperspectral image classification. In: 2017 IEEE International Conference on Image Processing (ICIP). pp. 3904–3908. IEEE (2018)

    Google Scholar 

  9. Gu, Y., Liu, T., Jia, X., et al.: Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 54(6), 3235–3247 (2016)

    Article  Google Scholar 

  10. Jia, S., Lin, Z., Deng, B., et al.: Cascade superpixel regularized gabor feature fusion for hyperspectral image classification. IEEE Trans. Neural Netw. Learn. Syst. 31(5), 1638–1652 (2019)

    Article  Google Scholar 

  11. Jia, S., Deng, B., Zhu, J., et al.: Local binary pattern-based hyperspectral image classification with superpixel guidance. IEEE Trans. Geosci. Remote Sens. 56(2), 749–759 (2017)

    Article  Google Scholar 

  12. He, Z., Li, J., Liu, K., et al.: Kernel low-rank multitask learning in variational mode decomposition domain for multi-hyperspectral classification. IEEE Trans. Geosci. Remote Sens. 56(7), 4193–4208 (2018)

    Article  Google Scholar 

  13. Li, F., Wang, J., Lan, R., et al.: Hyperspectral image classification using multi-feature fusion. Opt. Laser Technol. 110, 176–183 (2019)

    Article  Google Scholar 

  14. Zhang, C., Han, M., Xu, M.: Multi-feature classification of hyperspectral image via probabilistic SVM and guided filter. In: 2018 International Joint Conference on Neural Networks (IJCNN). pp. 1–7. IEEE (2018)

    Google Scholar 

  15. Liu, X., Yin, X., Cai, Y., et al.: Visual saliency-based extended morphological profiles for unsupervised feature learning of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 17(11), 1963–1967 (2019)

    Article  Google Scholar 

  16. Zhou, Z., Feng, J.: Deep forest: towards an alternative to deep neural networks. In: 2017 International Joint Conference on Artificial Intelligence Organization (IGCAI). pp. 3553–3559

    Google Scholar 

  17. Yin, X., Wang, R. et al.: Deep forest-based classification of hyperspectral images. In: Chinese Control Conference, pp. 10367–10372, IEEE (2018)

    Google Scholar 

  18. Liu, X., Wang, R., Cai, Z., et al.: Deep multigrained cascade forest for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 57(10), 8169–8183 (2019). https://doi.org/10.1109/TGRS.2019.2918587

    Article  Google Scholar 

  19. Luo, H., Tang, Y.Y., Yang, X., et al.: Autoencoder with extended morphological profile for hyperspectral image classification. In: 2017 IEEE International Conference on Cybernetics (CYBCONF). pp. 293–296. IEEE (2017)

    Google Scholar 

  20. Zhang, J., Sclaroff, S.: Saliency detection: a Boolean map approach. In: 2013 IEEE International Conference on Computer Vision (ICCV). pp. 153–160. IEEE (2013)

    Google Scholar 

  21. Guo, G., Neagu, D., Huang, X., Bi, Y.: An effective combination of multiple classifiers for toxicity prediction. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) Fuzzy Systems and Knowledge Discovery, pp. 481–490. Springer Berlin Heidelberg, Berlin, Heidelberg (2006). https://doi.org/10.1007/11881599_56

    Chapter  Google Scholar 

  22. Zhu, L., Chen, Y., Ghamisi, P., Benediktsson, J.A.: Generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(9), 5046–5063 (2018)

    Article  Google Scholar 

  23. Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework. IEEE Trans. Geosci. Remote Sens. 56(2), 847–858 (2018)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by National Nature Science Foundation of China (Grant Nos. 61973285, 62076226, 61873249).

Author information

Authors and Affiliations

  1. School of Automation, China University of Geosciences, Wuhan, 430074, China

    Xiaobo Liu, Mostofa Zaman Mohammad, Chaochao Zhang & Xin Gong

  2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China

    Xiaobo Liu, Mostofa Zaman Mohammad, Chaochao Zhang & Xin Gong

  3. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China

    Xiaobo Liu, Mostofa Zaman Mohammad, Chaochao Zhang & Xin Gong

  4. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Zhihua Cai

Authors
  1. Xiaobo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mostofa Zaman Mohammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaochao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhihua Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaobo Liu .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Taiyuan University of Science and Technology, Taiyuan, China

    Zhihua Cui

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Jianghui Cai

  4. Huazhong University of Science and Technology, Wuhan, China

    Lianghao Li

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, X., Mohammad, M.Z., Zhang, C., Gong, X., Cai, Z. (2022). Multi-feature Fusion Based Deep Forest for Hyperspectral Image Classification. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1566. Springer, Singapore. https://doi.org/10.1007/978-981-19-1253-5_22

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-1253-5_22

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1252-8

  • Online ISBN: 978-981-19-1253-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation