收稿：2019-06-10，
纸质出版：2020-04-07
移动端阅览
高光谱遥感影像具有光谱分辨率极高的特点，承载了大量可区分不同类型地物的诊断性光谱信息以及区分亚类相似地物之间细微差别的光谱信息，在目标探测领域具有独特的优势。与此同时，高光谱遥感影像也带来了数据维数高、邻近波段之间存在大量冗余信息的问题，高维度的数据结构往往使得高光谱影像异常目标类和背景类之间的可分性降低。为了缓解上述问题，本文提出了一种基于波段选择的协同表达高光谱异常探测算法。首先，使用最优聚类框架对高光谱波段进行选择，获得一组波段子集来表示原有的全部波段，使得高光谱影像异常目标类与背景类之间的可分性增强。然后使用协同表达对影像上的像元进行重建，由于异常目标类和背景类之间的可分性增强，对异常目标像元进行协同表达时将会得到更大的残差，异常目标像元的输出值增大，可以更好地实现异常目标和背景类的分离。本文使用了3组高光谱影像数据进行异常目标探测实验，实验结果表明，该方法与其他现有高光谱异常目标探测算法对比， 曲线下面积AUC（Area Under Curve）值更高，可以更好地实现异常目标与背景分离，能够更有效地对高光谱影像进行异常目标探测。
Hyperspectral images with high spectral resolution contain hundreds and even thousands of spectral bands and convey abundant spectral information to distinguish subtle spectral differences
especially between similar materials
thereby providing unique advantage for target detection. At the same time
hyperspectral images cause large number of bands and redundant information between adjacent bands. The high-dimensional data structure frequently reduces the separability between the anomaly and background classes of hyperspectral images. This study proposed a Band Selection-based Collaborative Representation (BSCR) method for hyperspectral anomaly detection to overcome these shortcomings.
In BSCR
we first selected hyperspectral bands via an optimal clustering framework and obtained a set of representative bands. The separability between the anomaly and background classes enhanced. Then
we reconstructed each pixel in the image through collaborative representation. We obtained a large residual when reconstructing the anomaly pixel via collaborative representation and achieved a large output value for an anomaly pixel because of the enhanced separability between the anomaly and background classes
thereby improving the separation of the anomaly class from the background class.
The proposed algorithm was tested on synthetic and real hyperspectral images. The experimental results of three hyperspectral images show that the proposed BSCR demonstrates outstanding detection performance in the receiver operating characteristic curve
area under the curve value
and separability map compared with other state-of-the-art detectors.
BSCR has an improved discriminative ability to separate the target from the background. Compared with several traditional anomaly detection algorithms
BSCR enhances the separability between the target and background through band selection and can effectively separate the anomaly class from the background class in hyperspectral images. Algorithms
such as PCAroCRD
which also conduct collaborative representation after the dimensionality reduction of hyperspectral images
can remove certain anomaly pixels and make the background construction stable. However
the dimensionality reduction mode of such algorithms will change the original signal of the image
thereby making it smooth and increasing the difficulty to distinguish between anomaly and background classes. The dimensionality reduction method of the band selection in BSCR can enhance the separability between the target and background without changing the original signal of the image
thereby making it easy to distinguish when detecting anomaly pixels and enabling sufficient discriminative ability to separate the target from the background.
Acito N , Matteoli S , Rossi A , Diani M and Corsini G . 2016 . Hyperspectral airborne “viareggio 2013 trial” data collection for detection algorithm assessment . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 ( 6 ): 2365 - 2376 [ DOI: 10.1109/JSTARS.2016.2531747 http://dx.doi.org/10.1109/JSTARS.2016.2531747 ]
Banerjee A , Burlina P and Diehl C . 2006 . A support vector method for anomaly detection in hyperspectral imagery . IEEE Transactions on Geoscience and Remote Sensing , 44 ( 8 ): 2282 - 2291 [ DOI: 10.1109/tgrs.2006.873019 http://dx.doi.org/10.1109/tgrs.2006.873019 ]
Billor N , Hadi A S and Velleman P F . 2000 . BACON: blocked adaptive computationally efficient outlier nominators . Computational Statistics and Data Analysis , 34 ( 3 ): 279 - 298 [ DOI: 10.1016/s0167-9473(99)00101-2 http://dx.doi.org/10.1016/s0167-9473(99)00101-2 ]
Carlotto M J . 2005 . A cluster-based approach for detecting man-made objects and changes in imagery . IEEE Transactions on Geoscience and Remote Sensing , 43 ( 2 ): 374 - 387 [ DOI: 10.1109/tgrs.2004.841481 http://dx.doi.org/10.1109/tgrs.2004.841481 ]
Chang C I and Chang S S . 2002 . Anomaly detection and classification for hyperspectral imagery . IEEE Transactions on Geoscience and Remote Sensing , 40 ( 6 ): 1314 - 1325 [ DOI: 10.1109/TGRS.2002.800280 http://dx.doi.org/10.1109/TGRS.2002.800280 ]
Chang S Z , Du B and Zhang L P . 2018 . BASO: a background-anomaly component projection and separation optimized filter for anomaly detection in hyperspectral images . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 7 ): 3747 - 3761 [ DOI: 10.1109/TGRS.2018.2810124 http://dx.doi.org/10.1109/TGRS.2018.2810124 ]
Dong Y N , Du B , Zhang L F and Zhang L P . 2018 . Hyperspectral target detection based on locally adaptive information-theoretic metric learning method . Geomatics and Information Science of Wuhan Univers , 43 ( 8 ): 1271 - 1277
董燕妮 , 杜博 , 张乐飞 , 张良培 . 2018 基于局域自适应信息理论测度学习的高光谱目标探测方法 . 武汉大学学报·信息科学版 , 43 ( 8 ): 1271 - 1277 ） [ DOI: 10.13203/j.whugis20150504 http://dx.doi.org/10.13203/j.whugis20150504 ]
Hughes G . 1968 . On the mean accuracy of statistical pattern recognizers . IEEE Transactions on Information Theory , 14 ( 1 ): 55 - 63 [ DOI: 10.1109/TIT.1968.1054102 http://dx.doi.org/10.1109/TIT.1968.1054102 ]
Kang X D , Zhang X P , Li S T , Li K L , Li J and Benediktsson J A . 2017 . Hyperspectral anomaly detection with attribute and edge-preserving filters . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 10 ): 5600 - 5611 [ DOI: 10.1109/tgrs.2017.2710145 http://dx.doi.org/10.1109/tgrs.2017.2710145 ]
Kwon H , Der S Z and Nasrabadi N M . 2003 . Dual-window-based anomaly detection for hyperspectral imagery // Proceedings of SPIE Automatic Target Recognition XIII . Orlando, Florida, United States : SPIE [DOI: 10.1117/12.486056]
Kwon H and Nasrabadi N M . 2005 . Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery . IEEE Transactions on Geoscience and Remote Sensing , 43 ( 2 ): 388 - 397 [ DOI: 10.1109/tgrs.2004.841487 http://dx.doi.org/10.1109/tgrs.2004.841487 ]
Li w Du Q . 2015 . Collaborative representation for hyperspectral anomaly detection . IEEE Transactions on Geoscience and Remote Sensing , 53 ( 3 ): 1463 - 1474 [ DOI: 10.1109/tgrs.2014.2343955 http://dx.doi.org/10.1109/tgrs.2014.2343955 ]
Manolakis D and Shaw G . 2002 . Detection algorithms for hyperspectral imaging applications . IEEE Signal Processing Magazine , 19 ( 1 ): 29 - 43 [ DOI: 10.1109/79.974724 http://dx.doi.org/10.1109/79.974724 ]
Nasrabadi N M . 2014 . Hyperspectral target detection: an overview of current and future challenges . IEEE Signal Processing Magazine , 31 ( 1 ): 34 - 44 [ DOI: 10.1109/MSP.2013.2278992 http://dx.doi.org/10.1109/MSP.2013.2278992 ]
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 [ DOI: 10.1109/29.60107 http://dx.doi.org/10.1109/29.60107 ]
Schaum A P . 2007 . Hyperspectral anomaly detection beyond RX . Proceedings of SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII. Orlando, Florida, United States : SPIE : 656502 [ DOI: 10.1117/12.718789 http://dx.doi.org/10.1117/12.718789 ]
Su H J , Wu Z Y , Du Q and Du P J . 2018 . Hyperspectral anomaly detection using collaborative representation with outlier removal . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 11 ( 12 ): 5029 - 5038 [ DOI: 10.1109/jstars.2018.2880749 http://dx.doi.org/10.1109/jstars.2018.2880749 ]
Vane G , Goetz A F H . 1988 . Terrestrial imaging spectroscopy . Remote Sensing of Environment , 24 ( 1 ): 1 - 29 [ DOI: 10.1016/0034-4257(88)90003-X http://dx.doi.org/10.1016/0034-4257(88)90003-X ]
Wang Q , Zhang F H and Li X L . 2018 . Optimal clustering framework for hyperspectral band selection . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 10 ): 5910 - 5922 [ DOI: 10.1109/tgrs.2018.2828161 http://dx.doi.org/10.1109/tgrs.2018.2828161 ]
Zhang L F , Zhang L P , Tao D C , Huang X and Du B . 2014 . Hyperspectral remote sensing image subpixel target detection based on supervised metric learning . IEEE Transactions on Geoscience and Remote Sensing , 52 ( 8 ): 4955 - 4965 [ DOI: 10.1109/tgrs.2013.2286195 http://dx.doi.org/10.1109/tgrs.2013.2286195 ]
Zhang L F , Zhang L P and Tao D C . 2010 . Tensor-based learning machine for remotely sensed image target detection . Journal of Remote Sensing . 14 ( 3 ): 519 — 533
张乐飞 , 张良培 , 陶大程 . 2010 . 张量分类算法的遥感影像目标探测 . 遥感学报 , 14 ( 3 ): 519-533 ） [ DOI: 10.11834/jrs.20100310 http://dx.doi.org/10.11834/jrs.20100310 ]
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 [ DOI: 10.1109/JSTARS.2014.2311995 http://dx.doi.org/10.1109/JSTARS.2014.2311995 ]
Zhao R , Du B , Zhang L P and Zhang L F . 2016a . Beyond background feature extraction: an anomaly detection algorithm inspired by slowly varying signal analysis . IEEE Transactions on Geoscience and Remote Sensing , 54 ( 3 ): 1757 - 1774 [ DOI: 10.1109/TGRS.2015.2488285 http://dx.doi.org/10.1109/TGRS.2015.2488285 ]
Zhao R , Du B , Zhang L P and Zhang L F . 2016b . A robust background regression based score estimation algorithm for hyperspectral anomaly detection . ISPRS Journal of Photogrammetry and Remote Sensing , 122 : 126 - 144 [ DOI: 10.1016/j.isprsjprs.2016.10.006 http://dx.doi.org/10.1016/j.isprsjprs.2016.10.006 ]
Zhao R , Du B and Zhang L P . 2017 . Hyperspectral anomaly detection via a sparsity score estimation framework . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 6 ): 3208 - 3222 [ DOI: 10.1109/TGRS.2017.2664658 http://dx.doi.org/10.1109/TGRS.2017.2664658 ]
Zhao R and Zhang L P . 2017 . GSEAD: graphical scoring estimation for hyperspectral anomaly detection . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 10 ( 2 ): 725 - 739 [ DOI: 10.1109/JSTARS.2016.2602278 http://dx.doi.org/10.1109/JSTARS.2016.2602278 ]
Zhu D H , Du B and Zhang L P . 2019 . Target dictionary construction-based sparse representation hyperspectral target detection methods . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 12 ( 4 ): 1254 - 1264 [ DOI: 10.1109/jstars.2019.2902430 http://dx.doi.org/10.1109/jstars.2019.2902430 ]
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