سابِق|تال

Saliency weighted RX hyperspectral imagery anomaly detection

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optical and Precision Machinery, Chinese Academy of Sciences, Xi’an 710119, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：liujiacheng215@mails.ucas.ac.cn
  • Introduction：刘嘉诚，1993年生，男，硕士研究生，研究方向为高光谱图像处理。E-mail：liujiacheng215@mails.ucas.ac.cn
Jiacheng LIU12,  
  • Affiliation：

    Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optical and Precision Machinery, Chinese Academy of Sciences, Xi’an 710119, China

Shuang WANG1,  
  • Affiliation：

    Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optical and Precision Machinery, Chinese Academy of Sciences, Xi’an 710119, China

Weihua LIU1,  
  • Affiliation：

    Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optical and Precision Machinery, Chinese Academy of Sciences, Xi’an 710119, China

Bingliang HU1

ملخص

With the development of spectral imaging technique and its data processing technology, anomaly detection using hyperspectral data has become a popular topic. Anomaly detection refers to the search for sparse pixels of unknown spectral signals in hyperspectral imagery. Given that the anomaly detection is unsupervised, providing a priori information is necessary. Thus, anomaly detection has a strong practicality. Considering the lack of spatial correlation and low normal distribution adaptation, the traditional RX algorithm has an inaccurate background estimation. Thus, this algorithm is unsuitable for detecting hyperspectral data. In this study, a saliency weighted RX algorithm is proposed on the basis of the local neighborhood spectra of an image. When the human eye observes an image, the first object that is viewed is frequently the most significant. The significance of the saliency detection algorithm is to identify this goal. The saliency map is a 2D image of the same size as the original image to represent the significance of the corresponding pixel in the original image. In this algorithm, the image background modeling based on probability density is improved by introducing a saliency analysis method. Afterward, the spectral saliency map is established, and the mean vector and covariance matrix of the RX algorithm are redefined. Saliency weighted RX algorithm provides different weights to optimize the background estimation. Anomaly detection experiments are conducted using synthetic and real hyperspectral data. Synthetic data experimental results show that, for each target, the number of anomalies detected using the saliency weighted RX algorithm is more than that of the traditional algorithms, and the saliency weighted RX algorithm can detect anomalies with abundance below 0.1. By contrast, traditional algorithms cannot detect these anomalies. Moreover, the false alarm pixels of the traditional algorithms are distributed in various positions, whereas the saliency weighted RX algorithm concentrates on an area called a false alarm area. This area can be removed effectively by morphological filtering. Real data experimental results show that the saliency weighted RX algorithm corresponds to the largest AUC value and has the optimal detection results. The traditional RX algorithm assumes that the background model follows a multivariate Gaussian distribution and does not perform well in hyperspectral imagery. The method of saliency analysis in the field of computer vision can be effectively analyzed in the spatial domain. This phenomenon compensates for the shortcomings of the RX algorithm to ignore spatial correlation, thus detecting the anomalies synchronized in the spatial and spectral domains. The saliency weighted RX algorithm uses a saliency analysis method to provide the background and anomaly pixels with a different weight, thereby improving the adaptability of the background model. Through the experiment of synthetic and real data, the saliency weighted algorithm can improve the detection probability while reducing the false alarm rate in comparison with the traditional RX algorithm and has a certain anti-noise ability.

مفهوم

anomaly detection;saliency;RX algorithm;hyperspectral image processing

References

  1. 1.
    Ashton E A and Schaum A. 1998. Algorithms for the detection of sub-pixel targets in multispectral imagery. Photogrammetric Engineering and Remote Sensing, 64(7): 723–731
  2. 2.
    Chang C I and Chiang S S. 2002. Anomaly detection and classification for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 40(6): 1314–1325
  3. 3.
    Chang C I, Li Y, Hobbs M C, Schultz R C and Liu W M. 2015. Progressive band processing of anomaly detection in hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7): 3558–3571
  4. 4.
    Gao K, Liu Y, Wang L J, Zhu Z Y and Cheng H B. 2015. A hyperspectral imagery anomaly detection algorithm based on gauss-Markov model. Spectroscopy and Spectral Analysis, 35(10): 2846–2850 (
  5. 5.
    Goferman S, Zelnik-Manor L, Tal A. 2012. Context-aware saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(10): 1915–1926
  6. 6.
    Guo Q D, Zhang B, Qiong R, Gao L R, Li J and Plaza A. 2014. Weighted-RXD and linear filter-based RXD: improving background statistics estimation for anomaly detection in hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2351–2366
  7. 7.
    Itti L, Koch C, Niebur E. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11): 1254–1259
  8. 8.
    Khazai S, Homayouni S, Safari A and Mojaradi B. 2011. Anomaly detection in hyperspectral images based on an adaptive support vector method. IEEE Geoscience and Remote Sensing Letters, 8(4): 646–650
  9. 9.
    Li J H and Liu Z. 2015. Object detection based on visual saliency map and objectness. Journal of Computer Applications, 35(12): 3560–3564 (
  10. 10.
    Liu W M and Chang C I. 2013. Multiple-window anomaly detection for hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2): 644–658
  11. 11.
    Molero J M, Garzón E M, García I and Plaza A. 2013. Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2): 801–814
  12. 12.
    Reed I S and Yu X. 1990. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(10): 1760–1770
  13. 13.
    Stellman C M, Hazel G G, Bucholtz F, Michalowicz J V, Stocker A D and Schaaf W. 2000. Real-time hyperspectral detection and cuing. Optical Engineering, 39(7): 1928–1935
  14. 14.
    Tong Q X, Zhang B and Zheng L F. 2006. Hyperspectral Remote Sensing: Principle, Technology and Application. Beijing: Higher Education Press: 40–47
  15. 15.
    Tong Q X, Zhang B and Zhang L F. 2016. Current progress of hyperspectral remote sensing in China. Journal of Remote Sensing, 20(5): 689–707 (
  16. 16.
    Xu Y, Wu Z B, Li J, Plaza A and Wei Z H. 2016. Anomaly detection in hyperspectral images based on low-rank and sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 1990–2000
  17. 17.
    Yu X, Reed I S and Stocker A D. 1993. Comparative performance analysis of adaptive multispectral detectors. IEEE Transactions on Signal Processing, 41(8): 2639–2656
  18. 18.
    Yuan Y, Wang Q and Zhu G K. 2015. Fast hyperspectral anomaly detection via high-order 2-D crossing filter. IEEE Transactions on Geoscience and Remote Sensing, 53(2): 620–630
  19. 19.
    Zhang B. 2016. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20(5): 1062–1090 (
  20. 20.
    Zhao C H, Wang L G and Qi B. 2016. Hyperspectral Remote Sensing Images Processing Methods and Applications. Beijing: Publishing House of Electronics Industry: 261–271
  21. 21.
    Zhao C H, Yao X F. 2016. Local kernel RX algorithm-based hyperspectral real-time detection. Journal of Infrared and Millimeter Waves, 35(6): 708–714 (
  22. 22.
    Zhao R, Du B and Zhang L P. 2014. A robust nonlinear hyperspectral anomaly detection approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4): 1227–1234

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