• Acta Photonica Sinica
  • Vol. 47, Issue 7, 710001 (2018)
ZHAO Liao-ying1、*, LIN Wei-jun1, WANG Yu-lei2、3, and LI Xiao-run4
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
  • 4[in Chinese]
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    DOI: 10.3788/gzxb20184707.0710001 Cite this Article
    ZHAO Liao-ying, LIN Wei-jun, WANG Yu-lei, LI Xiao-run. Non-casual Real-time RXD Detection for Hyperspectral Imagery Based on Sliding Array[J]. Acta Photonica Sinica, 2018, 47(7): 710001 Copy Citation Text show less
    References

    [2] ZHANG Liang-pei. Advance and future challenges in hyperspectral target detection[J]. Geomatics and Information Science of Wuhan University, 2014, 39(12): 1387-1394.

    [3] ZHAO Chun-hui, LI Xiao-hui, TIAN Ming-hua. Hyperspectral imaging abnormal target detection algorithm using principal component quantization and density estimation on EM clustering[J] Acta Photonica Sinica, 2013, 42(10): 1224-1230

    [4] REED I S, YU X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE Transactions on Acoustics Speech and Signal Processing, 1990, 38(10): 1760-1770.

    [5] CHANG C I, CHIANG S S. Anomaly detection and classification for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(6): 1314-1325.

    [6] GOOVAERTS P. Detection of local anomalies in high resolution hyperspectral imagery using geostatistical filtering and local spatial statistics[M]. Springer Netherlands: Geostatistics Banff, 2005.

    [7] REN H, CHEN C W, CHEN H T. Weighted anomaly detection for hyperspectral remotely sensed images[J]. Applied Mechanics and Materials, 2005, 643: 228-232.

    [8] DU B, ZHAO R, ZHANG L, et al. A spectral-spatial based local summation anomaly detection method for hyperspectral images[J]. Signal Processing, 2016, 124: 115-131.

    [9] REN Xiao-dong, LEI Wu-hu. Kernel anomaly detection method in hyperspectral imagery based on the spectral discrimination method[J]. Acta Photonica Sinica, 2016, 45(3): 0330003

    [10] CHEN S Y, WANG Y, WU C C, et al. Real-time causal processing of anomaly detection for hyperspectral imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1511-1534.

    [11] ZHAO Chun-hui, YOU Wei, QI Bin, et al. Real-time anomaly detection algorithm for hyperspec tral remote sensing by using recursive polynomial kernel function[J]. Acta Photonica Sinica, 2016, 36(2): 257- 265

    [12] MOLERO J M, GARZN E M, GARCA I, et al. Analysis and optimizations of global and local versions of the rx algorithm for anomaly detection in hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 801-814.

    [13] CHANG C I. Anomaly detection using causal sliding windows[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 8(7): 3260-3270.

    [14] ZHANG Li-fu, PENG Bo, ZHANG Fei-zhou, et al. Fast real-time causal linewise progressive hyperspectral anomaly detection via cholesky decomposition[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(10): 4614-4629.

    [15] ZHAO Chun-hui, DENG Wei-wei, YAO Xi-feng. Hyperspectral real-time anomaly target detection based on progressive line processing[J]. Acta Photonica Sinica, 2017, 37(1): 0128002

    [17] WANG Yu-lei. Real-time target detection algorithms for hyperspectral imagery[D]. Harbin: Harbin Engineering University, 2015.

    [18] WEI Xiu-xi, ZHOU Yong-quan. A new performance categoriesvevaluation method based on ROC curve[J]. Computer Technology and Development, 2010, 20(11): 47-50.

    ZHAO Liao-ying, LIN Wei-jun, WANG Yu-lei, LI Xiao-run. Non-casual Real-time RXD Detection for Hyperspectral Imagery Based on Sliding Array[J]. Acta Photonica Sinica, 2018, 47(7): 710001
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