Acta Photonica Sinica, Volume. 50, Issue 9, 0910002(2021)

Hyperspectral Abnormal Target Detection Based on Extended Multi-attribute Profile and Fast Local RX Algorithm

Ruhan A1, Xiaobin YUAN2, Xiaodong MU1, and Jingyi WANG3
Author Affiliations
  • 1College of Operational Support, Rocket Force University of Engineering, Xi'an70025, China
  • 2Xi 'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences710119, China
  • 3School of Computer Science, Xi'an Shiyou University, Xi'an710065, China
  • show less
    References(22)

    [1] XU Dongdong, CHENG Deqiang, CHEN Liangliang等. Hyperspectral image classification based on hierarchical guidance filtering and nearest regularized subspace[J]. Acta Photonica Sinica, 49(2020).

    [2] GUO Liqiang, MENG Qingchao. Space spectrum classification algorithm based on multi-label shared subspace learning and kernel ridge regression[J]. Acta Photonica Sinica, 49(2020).

    [3] MA Shixin, LIU Chuntong, LI Hongcai等. Improved collaborative algorithm based on spatial-spectral joint clustering for hyperspectral anomaly detection[J]. Acta Photonica Sinica, 48(2019).

    [4] WANG Zhiwei, TAN Kun, WANG Xue等. Unsupervised nearest regularized subspace based on spectral space reconstruction for hyperspectral anomaly detection[J]. Acta Photonica Sinica, 49(2020).

    [5] XUE Qingsheng, TIAN Zhongtian, YANG Bai等. Optical system design of geostationary hyperspectal ocean water color imager with wide coverage[J]. Acta Photonica Sinica, 49(2020).

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

    [7] LI Z, ZHANG Y. Hyperspectral anomaly detection based on improved RX with CNN framework[J]. IEEE International Symposium on Geoscience and Remote Sensing IGARSS, 2244-2247(2019).

    [8] HIDALGO J A P, PEREZ-SUAY A et al. Efficient nonlinear RX anomaly detectors[J]. IEEE Geoscience and Remote Sensing Letters, 18, 231-235(2021).

    [9] REN L, ZHAO L, WANG Y. A superpixel-based dual window RX for hyperspectral anomaly detection[J]. IEEE Geoscience and Remote Sensing Letters, 17, 1233-1237(2020).

    [10] IMANI M. RX anomaly detector with rectified background[J]. IEEE Geoscience and Remote Sensing Letters, 14, 1313-1317(2017).

    [11] LI W, DU Q. Collaborative representation for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 1463-1474(2015).

    [12] MA N, PENG Y, WANG S. A fast recursive collaboration representation anomaly detector for hyperspectral image[J]. IEEE Geoscience and Remote Sensing Letters, 16, 588-592(2019).

    [13] WU Z, SU H, ZHENG P. hyperspectral anomaly detection using collaborative representation with PCA remove outlier[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1-5(2018).

    [14] SUN W, LIU C, LI J et al. Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery[J]. Journal of Applied Remote Sensing, 8, 83641(2014).

    [15] ZHANG Y, DU B, ZHANG L et al. A low-rank and sparse matrix decomposition-based Mahalanobis distance method for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 1-14(2016).

    [16] SONG S, YANG Y, ZHOU H et al. Hyperspectral anomaly detection via graph dictionary-based low rank decomposition with texture feature extraction[J]. Remote Sensing, 12, 2072-4292(2020).

    [17] BENEDIKTSSON J A, PALMASON J A, SVEINSSON J R. Classification of hyperspectral data from urban areas based on extended morphological profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 43, 480-491(2005).

    [18] DALLA MURA M, BENEDIKTSSON J A, WASKE B et al. Morphological attribute profiles for the analysis of very high resolution images[J]. IEEE Transactions on Geoscience and Remote Sensing, 48, 3747-3762(2010).

    [19] DALLA MURA M, BENEDIKTSSON J A, WASKE B et al. Extended profiles with morphological attribute filters for the analysis of hyperspectral data[J]. International Journal of Remote Sensing, 31, 5975-5991(2010).

    [20] ANDIKA F, RIZKINIA M. A Hyperspectral anomaly detection algorithm based on morphological profile and attribute filter with band selection and automatic determination of maximum area[J]. Remote Sensing, 12, 3387(2020).

    [21] HIGHAM N J[M]. Accuracy and stability of numerical algorithms(2002).

    [22] KANG X, ZHANG X, LI S et al. Hyperspectral anomaly detection with attribute and Edge-Preserving filters[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 5600-5611(2017).

    Tools

    Get Citation

    Copy Citation Text

    Ruhan A, Xiaobin YUAN, Xiaodong MU, Jingyi WANG. Hyperspectral Abnormal Target Detection Based on Extended Multi-attribute Profile and Fast Local RX Algorithm[J]. Acta Photonica Sinica, 2021, 50(9): 0910002

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Mar. 8, 2021

    Accepted: May. 6, 2021

    Published Online: Oct. 22, 2021

    The Author Email:

    DOI:10.3788/gzxb20215009.0910002

    Topics