Computer Science ›› 2018, Vol. 45 ›› Issue (6): 259-264.doi: 10.11896/j.issn.1002-137X.2018.06.046

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Real-time Sub-pixel Object Detection for Hyperspectral Image Based on Pixel-by-pixel Processing

LIN Wei-jun1, ZHAO Liao-ying1, LI Xiao-run2   

  1. School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018,China1;
    College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China2
  • Received:2017-04-12 Online:2018-06-15 Published:2018-07-24

Abstract: Sub-pixel target detection is one of the key technologies in the applications of hyperspectral images.Since the high dimensions of hyperspectral data increase apparently the storage space and complexity of data processing,real-time processing has become a crucial problem for target detection.Adaptive matched filter (AMF) is an effective algorithm for sub-pixel target detection.This paper derived the real-time AMF target detection procedure of hyperspectral images by using AMF as the sub-pixel target detection algorithm,based on the realization of real-time inversing of hyperspectral data’s covariance matrix with the pixel-by-pixel format transmission and storage by using Woodbury lemma.Expe-riments were conducted on synthetic data and real hyperspectral images.The results demonstrate that compared with non-real time AMF,real-time AMF needs less storage space and can achieve the same or slightly better detection accuracy.

Key words: Hyperspectral image processing, Pixel-by-pixel processing, Sub-pixel target detection, Causal processing, Adaptive matched filter

CLC Number: 

  • TP391
[1]姜志侠,孟品超,李延忠.矩阵分析[M].北京:清华大学出版社,2015.
[2]CHEN S Y,WANG Y L,WU C C,et al.Real-Time Causal Processing of Anomaly Detection for Hyperspectral Imagery [J].IEEE Transactions on Geoscience and Remote Sensing,2014,50(2):1513-1534.
[3]CHANG C I,LI H C,SONG M P,et al.Real-Time Constrained Energy Minimization for Subpixel Detection [J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(6):2545-2559.
[4]CHANG C I,SCHULTZ R C,HOBBS M C,et al.Progressive Band Processing of Constrained Energy Minimization for Subpixel Detection[J].IEEE Transactions on Geoscience and Remote Sensing,2015,50(3):1626-1637.
[5]ZHAO C H,WANG Y L,LI X H.A real-time anomaly detection algorithm for hyperspectral imagery based on causal processing [J].Journal of Infrared and Millimeter Waves,2015,34(1):114-121.(in Chinese)
赵春晖,王玉磊,李晓慧.一种新型高光谱实时异常检测算法[J].红外与毫米波学报,2015,34(1):114-121.
[6]ZHAO C H,YOU W.Real-Time Anomaly Detection Algorithm for Hyperspectral Remote Sensing by Using Recursive Polynomial Kernel Function [J].Acta Optica Sinica,2016(2):257-265.(in Chinese)
赵春晖,尤伟.采用多项式递归核的高光谱遥感异常实时检测算法[J].光学学报,2016(2):257-265.
[7]YANG B M,PLAZA A,GAO L,et al.Dual-Mode FPGA Implementation of Target and Anomaly Detection Algorithms for Real-Time Hyperspectral Imaging[J].IEEE Journal of Selected To-pics in Applied Earth Observations and Remote Sensing,2015,8(6):2950-2961.
[8]ZHANG L P.Advance and Future Challenges in Hyperspectral Target Detection [J].Geomatics and Information Science of Wuhan University,2014,39(12):1387-1394.(in Chinese)
张良培.高光谱目标探测的进展与前沿问题[J].武汉大学学报(信息科学版),2014,39(12):1387-1394.
[9]MANOLAKIS D,SHAW G.Detection algorithms forhyper-spectral imaging applications[J].IEEE Signal Process Maga-zing,2002,19(1):29-43.
[10]ROBEY F C,FUHRMANN D R,KELLY E J,et al.CFAR adaptive matched filter detector [J].IEEE Trans.on Aerospace and Electronic Systems,1992,28(1):208-218.
[11]GENG X R,JI L Y,SUN K.Clever eye algorithm for target detection of remote sensing imagery[J].Isprs Journal of Photogrammetry and Remote Sensing,2016,114:32-39.
[12]张兵,高连如.高光谱图像分类与目标检测[M].北京:科学出版社,2011.
[13]赵文吉,段福州,刘晓萌,等.ENVI遥感影像处理专题与实践 [M].北京:中国环境科学出版社,2007.
[14]CUI J T.Research on hyper spectral remote sensing image mixing technology [D].Hangzhou:Zhejiang University,2014.(in Chinese)
崔建涛.高光谱遥感图像解混技术研究[D].杭州:浙江大学,2014.
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