• Acta Photonica Sinica
  • Vol. 48, Issue 1, 110003 (2019)
MA Shi-xin*, LIU Chun-tong, LI Hong-cai, HE Zhen-xin, and WANG Hao
Author Affiliations
  • [in Chinese]
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    DOI: 10.3788/gzxb20194801.0110003 Cite this Article
    MA Shi-xin, LIU Chun-tong, LI Hong-cai, HE Zhen-xin, WANG Hao. Improved Collaborative Algorithm Based on Spatial-spectral Joint Clustering for Hyperspectral Anomaly Detection[J]. Acta Photonica Sinica, 2019, 48(1): 110003 Copy Citation Text show less
    References

    [1] GHAMISI P, YOKOYA N, LI J, et al. Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art[J]. IEEE Geoscience & Remote Sensing Magazine, 2018, 5(4): 37-78.

    [2] ZHU L, WEN G. Hyperspectral anomaly detection via background estimation and adaptive weighted sparse representation[J]. Remote Sensing, 2018, 10(2).

    [3] SOOFBAF S R, SAHEBI M R, MOJARADI B. A sliding window-based joint sparse representation (SWJSR) method forhyperspectral anomaly detection[J]. Remote Sensing, 2018, 10(3): 434.

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

    [5] TAITANO Y P, GEIER B A, BAUER K W. A locally adaptable iterative RXdetector[J]. Eurasip Journal on Advances in Signal Processing, 2010, 2010(1): 341908.

    [6] 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 & Remote Sensing, 2013, 6(2): 801-814.

    [7] IMANI M. RX anomaly detector with rectifiedbackground[J]. IEEE Geoscience & Remote Sensing Letters, 2017, 14(8): 1313-1317.

    [8] KWON H, NASRABADI N M. Kernel RX-algorithm: a nonlinear anomaly detector forhyperspectral imagery[J]. IEEE Transactions on Geoscience & Remote Sensing, 2005, 43(2): 388-397.

    [9] BANERJEE A, BURLINA P, DIEHL C. A support vector method for anomaly detection inhyperspectral imagery[J]. IEEE Transactions on Geoscience & Remote Sensing, 2006, 44(8): 2282-2291.

    [10] KHAZAI S, HOMAYOUNI S, SAFARI A, et al. Anomaly detection in hyperspectral images based on an adaptive support vector method[J]. IEEE Geoscience & Remote Sensing Letters, 2011, 8(4): 646-650.

    [11] LI W, DU Q. Collaborative representation forhyperspectral anomaly detection[J]. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(3): 1463-1474.

    [12] LI J, ZHANG H, ZHANG L, et al. Hyperspectral anomaly detection by the use of background joint sparse representation[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(6): 2523-2533.

    [13] ZHANG W, LI X, DOU Y, et al. A geometry-based band selection approach for hyperspectral image analysis[J]. IEEE Transactions on Geoscience & Remote Sensing, 2018, 56(8): 4318-4333.

    [14] SHEN J, HAO X, LIANG Z, et al. Real-time superpixel segmentation by DBSCAN clustering algorithm[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2016, 25(12): 5933-5942.

    [15] HOU J, GAO H, LI X. DSets-DBSCAN: a parameter-free clustering algorithm[J]. IEEE Transactions on Image Processing, 2016, 25(7): 3182-3193.

    [16] JIN Jing, ZOU Zheng-rong, TAO Chao. Compressed texton based high resolution remote sensing image classification[J]. Acta Geodaetica Et Cartographica Sinica, 2014, 43(5): 493-499.

    [17] VAFADAR M, GHASSEMIAN H. Anomaly detection ofhyperspectral imagery using modified collaborative representation[J]. IEEE Geoscience & Remote Sensing Letters, 2018(99): 1-5.

    [18] KHAZAI S, HOMAYOUNI S, SAFARI A,et al. Anomaly detection in hyperspectral images based on an adaptive support vector method[J]. IEEE Geoscience & Remote Sensing Letters, 2011, 8(4): 646-650.

    [19] TANG Yi-dong, HUANG Shu-lin, LING Qiang, et al. Adaptive kernel collaborative representation anomaly detection for hyperspectral imagery[J]. High Power Laser & Particle Beams, 2015, 27(9): 49-55.

    [20] MEI Feng, ZHAO Chun-hui, WANG Li-guo, et al. Support vector data description based on adaptive anomaly detection method in hyperspectral imagery[J]. Acta Photonica Sinica, 2009, 38(11): 2820-2825.

    MA Shi-xin, LIU Chun-tong, LI Hong-cai, HE Zhen-xin, WANG Hao. Improved Collaborative Algorithm Based on Spatial-spectral Joint Clustering for Hyperspectral Anomaly Detection[J]. Acta Photonica Sinica, 2019, 48(1): 110003
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