Skip to main content
SpringerLink
Log in

Ensemble of half-space trees for hyperspectral anomaly detection

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Most methods for hyperspectral anomaly detection (HAD) construct profiles of background pixels and identify pixels unconformable to the profiles as anomalies. Recently, isolation forest-based algorithms were introduced into HAD, which identifies anomalies from the background without background modeling. The path length is used as a metric to estimate the anomaly degree of a pixel, but it is not flexible and straightforward. This paper introduces the half-space tree (HS-tree) method from the theory of mass estimation into HAD and proposes a metric involving mass information and tree depth to measure the anomaly degree for each pixel. More specifically, the proposed HS-tree-based detection method consists of three main steps. First, the key spectral-spatial features are extracted using the principal component analysis and the extended morphological attribute profile methods. Then, the ensemble of HS-trees are trained using different randomly selected subsamples from the feature map. Finally, each instance in the feature map traverses through each HS-tree and the anomaly scores are computed as the final detection map. Compared with conventional methods, the experimental results on four real hyperspectral datasets demonstrate the competitiveness of our method in terms of accuracy and efficiency.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Gu Y F, Liu T Z, Gao G M, et al. Multimodal hyperspectral remote sensing: an overview and perspective. Sci China Inf Sci, 2021, 64: 121301

    Article  Google Scholar 

  2. Chen J, Ling Z C, Qiao L, et al. Mineralogy of Chang’e-4 landing site: preliminary results of visible and near-infrared imaging spectrometer. Sci China Inf Sci, 2020, 63: 140903

    Article  Google Scholar 

  3. Pu H Y, Wang B, Zhang L M. Simplex geometry-based abundance estimation algorithm for hyperspectral unmixing (in Chinese). Sci Sin Inform, 2012, 42: 1019–1033

    Google Scholar 

  4. Wang Q, He X, Li X. Locality and structure regularized low rank representation for hyperspectral image classification. IEEE Trans Geosci Remote Sens, 2019, 57: 911–923

    Article  Google Scholar 

  5. Hou Z F, Wei L, Tao R, et al. Collaborative representation with background purification and saliency weight for hyperspectral anomaly detection. Sci China Inf Sci, 2022, 65: 112305

    Article  Google Scholar 

  6. Eismann M T, Stocker A D, Nasrabadi N M. Automated hyperspectral cueing for civilian search and rescue. Proc IEEE, 2009, 97: 1031–1055

    Article  Google Scholar 

  7. Li X, Chen M, Nie F, et al. A multiview-based parameter free framework for group detection. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, 2017. 4147–4153

  8. Kruse F A, Boardman J W, Huntington J F. Comparison of airborne hyperspectral data and EO-1 hyperion for mineral mapping. IEEE Trans Geosci Remote Sens, 2003, 41: 1388–1400

    Article  Google Scholar 

  9. Gao Z, Shao Y, Xuan G, et al. Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning. Artif Intell Agr, 2020, 4: 31–38

    Google Scholar 

  10. Reed I S, Yu X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans Acoust Speech Signal Process, 1990, 38: 1760–1770

    Article  Google Scholar 

  11. Taitano Y P, Geier B A, Bauer K W. A locally adaptable iterative RX detector. EURASIP J Adv Signal Process, 2010, 2010: 341908

    Article  Google Scholar 

  12. Du B, Zhang L. Random-selection-based anomaly detector for hyperspectral imagery. IEEE Trans Geosci Remote Sens, 2011, 49: 1578–1589

    Article  Google Scholar 

  13. Gao L, Guo Q, Plaza A, et al. Probabilistic anomaly detector for remotely sensed hyperspectral data. J Appl Remote Sens, 2014, 8: 083538

    Article  Google Scholar 

  14. Kwon H, Nasrabadi N M. Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery. IEEE Trans Geosci Remote Sens, 2005, 43: 388–397

    Article  Google Scholar 

  15. Zhao R, Du B, Zhang L. A robust nonlinear hyperspectral anomaly detection approach. IEEE J Sel Top Appl Earth Observations Remote Sens, 2014, 7: 1227–1234

    Article  Google Scholar 

  16. Zhou J, Kwan C, Ayhan B, et al. A novel cluster kernel RX algorithm for anomaly and change detection using hyperspectral images. IEEE Trans Geosci Remote Sens, 2016, 54: 6497–6504

    Article  Google Scholar 

  17. Carlotto M J. A cluster-based approach for detecting man-made objects and changes in imagery. IEEE Trans Geosci Remote Sens, 2005, 43: 374–387

    Article  Google Scholar 

  18. Li J, Zhang H, Zhang L, et al. Hyperspectral anomaly detection by the use of background joint sparse representation. IEEE J Sel Top Appl Earth Observations Remote Sens, 2015, 8: 2523–2533

    Article  Google Scholar 

  19. Li W, Du Q. Collaborative representation for hyperspectral anomaly detection. IEEE Trans Geosci Remote Sens, 2015, 53: 1463–1474

    Article  Google Scholar 

  20. Sun W, Liu C, Li J, et al. Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery. J Appl Remote Sens, 2014, 8: 083641

    Article  Google Scholar 

  21. Xu Y, Wu Z, Li J, et al. Anomaly detection in hyperspectral images based on low-rank and sparse representation. IEEE Trans Geosci Remote Sens, 2016, 54: 1990–2000

    Article  Google Scholar 

  22. Wang W, Li S, Qi H, et al. Identify anomaly component by sparsity and low rank. In: Proceedings of the 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015. 1–4

  23. Qu Y, Guo R, Wang W, et al. Anomaly detection in hyperspectral images through spectral unmixing and low rank decomposition. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016. 1855–1858

  24. Qu Y, Wang W, Guo R, et al. Hyperspectral anomaly detection through spectral unmixing and dictionary-based low-rank decomposition. IEEE Trans Geosci Remote Sens, 2018, 56: 4391–4405

    Article  Google Scholar 

  25. Zhang X, Wen G, Dai W. A tensor decomposition-based anomaly detection algorithm for hyperspectral image. IEEE Trans Geosci Remote Sens, 2016, 54: 5801–5820

    Article  Google Scholar 

  26. Bati E, ćalışkan A, Koz A, et al. Hyperspectral anomaly detection method based on auto-encoder. In: Proceedings of SPIE, 2015. 220–226

  27. Ma N, Peng Y, Wang S, et al. An unsupervised deep hyperspectral anomaly detector. Sensors, 2018, 18: 693

    Article  Google Scholar 

  28. Li W, Wu G, Du Q. Transferred deep learning for anomaly detection in hyperspectral imagery. IEEE Geosci Remote Sens Lett, 2017, 14: 597–601

    Article  Google Scholar 

  29. Liu F, Ting K, Zhou Z. Isolation forest. In: Proceedings of the 8th IEEE International Conference on Data Mining, 2008. 413–422

  30. Zhang K, Kang X, Li S. Isolation forest for anomaly detection in hyperspectral images. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2019. 437–440

  31. Li S, Zhang K, Duan P, et al. Hyperspectral anomaly detection with kernel isolation forest. IEEE Trans Geosci Remote Sens, 2020, 58: 319–329

    Article  Google Scholar 

  32. Wang R, Nie F, Wang Z, et al. Multiple features and isolation forest-based fast anomaly detector for hyperspectral imagery. IEEE Trans Geosci Remote Sens, 2020, 58: 6664–6676

    Article  Google Scholar 

  33. Chang S, Du B, Zhang L. A subspace selection-based discriminative forest method for hyperspectral anomaly detection. IEEE Trans Geosci Remote Sens, 2020, 58: 4033–4046

    Article  Google Scholar 

  34. Ting K M, Zhou G T, Liu F T, et al. Mass estimation. Mach Learn, 2013, 90: 127–160

    Article  MathSciNet  MATH  Google Scholar 

  35. Bandaragoda T R, Ting K M, Albrecht D, et al. Isolation-based anomaly detection using nearest-neighbor ensembles. Comput Intell, 2018, 34: 968–998

    Article  MathSciNet  Google Scholar 

  36. Li X L, Chen M L, Nie F P, et al. Locality adaptive discriminant analysis. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017. 2201–2207

  37. Li X L, Zhao B. Video distillation (in Chinese). Sci Sin Inform, 2021, 51: 695–734

    Article  Google Scholar 

  38. Agarwal A, El-Ghazawi T, El-Askary H, et al. Efficient hierarchical-PCA dimension reduction for hyperspectral imagery. In: Proceedings of 2007 IEEE International Symposium on Signal Processing and Information Technology, 2007. 353–356

  39. Kang X, Zhang X, Li S, et al. Hyperspectral anomaly detection with attribute and edge-preserving filters. IEEE Trans Geosci Remote Sens, 2017, 55: 5600–5611

    Article  Google Scholar 

  40. Mura M D, Villa A, Benediktsson J A, et al. Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. IEEE Geosci Remote Sens Lett, 2011, 8: 542–546

    Article  Google Scholar 

  41. Mura M D, Benediktsson J A, Waske B, et al. Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans Geosci Remote Sens, 2010, 48: 3747–3762

    Article  Google Scholar 

  42. Breen E J, Jones R. Attribute openings, thinnings, and granulometries. Comput Vision Image Underst, 1996, 64: 377–389

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant No. QYZDY-SSW-JSC044) and National Natural Science Foundation of China (Grant No. 61871470).

Author information

Authors and Affiliations

  1. Shaanxi Key Laboratory of Ocean Optics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119, China

    Ju Huang & Xuelong Li

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Ju Huang

  3. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, 710072, China

    Xuelong Li

  4. Key Laboratory of Intelligent Interaction and Applications (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an, 710072, China

    Xuelong Li

Authors
  1. Ju Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuelong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuelong Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, J., Li, X. Ensemble of half-space trees for hyperspectral anomaly detection. Sci. China Inf. Sci. 65, 192103 (2022). https://doi.org/10.1007/s11432-021-3310-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-021-3310-x

Keywords

Search

Navigation