浏览全部资源
扫码关注微信
收稿日期：2021-03-09，
修回日期：2021-05-13，
录用日期：2021-5-20，
纸质出版日期：2021-08-16
移动端阅览
目的
2
由于在军事和民用应用中的重要作用，高光谱遥感影像异常检测在过去的20~30年里一直都是备受关注的研究热点。然而，考虑到异常点往往藏匿于大量的背景像元之中，且只占据很少的数量，给精确检测带来了不小的挑战。针对此问题，基于异常点往往表现在高频的细节区域这一前提，本文提出了一种基于异常点粗定位和协同表示的高光谱遥感影像异常检测算法。
方法
2
对输入的原始高光谱遥感影像进行空间维的降质操作；通过衡量降质后影像与原始影像在空间维的差异，粗略定位可能的异常点位置；将粗定位的异常点位置用于指导像元间的协同表示以重构像元；通过衡量重构像元与原始像元的差异，从而进一步优化异常检测结果。
结果
2
在4个数据集上与6种方法进行了实验对比。对于San Diego数据集，次优算法和本文算法分别取得的AUC（area under curve）值为0.978 6和0.994 0；对于HYDICE（hyperspectral digital image collection equipment）数据集，次优算法和本文算法的AUC值为0.993 6和0.998 5；对于Honghu数据集，次优算法和本文方法的AUC值分别为0.999 2和0.999 3；对Grand Isle数据集而言，尽管本文方法以0.001的差距略低于性能第1的算法，但从目视结果图中可见，本文方法所产生的虚警目标远少于性能第1的算法。
结论
2
本文所提出的粗定位和协同表示的高光谱异常检测算法，综合考虑了高光谱遥感影像的谱间特性，同时还利用了其空间特性以及空间信息的先验分布，从而获得异常检测结果的提升。
Objective
2
Hyperspectral image has rich spectral information. Different materials correspond to different spectral information
which can be applied to disaster warning
agriculture precision
and authenticity identification for some valuable art works. Anomaly detection of hyperspectral images refers to detecting the anomalous pixels in the scene without any prior information
and it is important in military and civil applications. In this way
the anomaly detection of hyperspectral images has gained increasing popularity. The anomalies usually refer to the outliers with spatial and spectral signatures that are severely different from their surroundings. Compared with the background
the anomalies have two main characteristics. First
their spectral information is severely different from that of their surroundings
and this phenomenon is named the spectral difference. Meanwhile
the anomalies are usually embedded into the local homogeneous background in a format of several pixels or even sub-pixels
and this phenomenon is called the spatial difference. Anomalies are often hidden in a large number of background pixels
and they only occupy a small number. Thus
they bring a great challenge to accurate detection. This study proposes a hyperspectral anomaly detection algorithm based on rough localization and collaborative representation of outliers to solve this problem. It is based on the institution that the anomalies often appear in high-frequency detail areas.
Method
2
A novel hyperspectral anomaly detection method based on the rough location and collaborative representation is proposed in this study. This method utilizes the spatial information and inter-spectral information carried by the hyperspectral images simultaneously
which ensures the accuracy of the algorithm. Three modules are included in the whole detection process. First
the original hyperspectral image is degraded in spatial dimension. Second
we can obtain the rough response map of spatial anomaly by measuring the difference between the degraded and original images in spatial dimension and locate the possible abnormal points according to the response value considering that the degradation operation of spatial dimension often loses high-frequency information. Finally
the rough location of outliers is used to guide the collaborative representation between pixels for reconstructing the center pixel. The detection result is further optimized by measuring the difference between the reconstructed center and original pixels. Experimental data contain four real-scenario datasets
namely
the San Diego
Grand Isle
hyperspectral digital image collection equipment(HYDICE)
and Honghu datasets. Experimental results demonstrate the effectiveness of the proposed method. Experimental comparison is made with six classical methods
namely
the Global-RX(Reed-Xiaoli) detector (RXD)
Local-RX detector (LRX)
collaborative representation-based detector (CRD)
tensor completion-based detector (TCD)
fractional Fourier estimation (FrFE)
and low-rank and sparse decomposition model with mixture of Gaussian (LSDM-MoG). The FrFE detector utilizes the fractional Fourier transformation to the spectral information
and it obtains the optimal order and the corresponding spectral feature. The spectral feature is further detected by the Reed-Xiaoli(RX) detector. In this way
the RXD
LRX
and the FrFE all belong to the statistical-based detectors. LSDM-MoG imports the mixture of Gaussian as a regularization term for the low-rank and sparse decomposition model
which is a typical representation-based anomaly detection method. In this way
the CRD
TCD
and the LSDM-MoG all belong to the representation-based detectors.
Result
2
We incorporate four real-scenario hyperspectral images to validate the performance of the proposed method. The quantitative evaluation metrics include the receiver operating curves and the area under the curve (AUC) value to evaluate the detection accuracy. Meanwhile
we also exhibit the detection maps of each method for visual comparison. The average results of the three datasets indicate that the second optimal mean AUC value (0.992 4) is achieved by the CRD detector. The corresponding mean AUC value achieved by the proposed method is 0.997 3. Compared with the algorithm with the second best performance
the AUC value for the San Diego dataset is increased from 0.978 6 to 0.994 0 by the proposed method. For the HYDICE dataset
the AUC value is increased from 0.996 3 to 0.998 5 by the proposed method compared with the detector with the second best performance. For the Honghu dataset depicting a long river bank in Honghu
Hubei Province of China
the proposed method achieves the AUC value of 0.999 3
which is superior than that of the detector with the second best performance. For the Grand Isle dataset
the AUC value of the proposed detector is slightly lower than that of the LSDM-MoG detector with the optimal performance by a gap of 0.001. However
the visual maps reveal that the false alarm targets generated by the LSDM-MoG are more frequent than those of the proposed method. Experimental results and data analysis demonstrate the effectiveness of the proposed algorithm.
Conclusion
2
A rough detection and collaborative representation-based algorithm for anomaly detection of hyperspectral images is proposed in this study. The anomalous and background pixels are coarsely separated by a simple spatial degradation processing. Meanwhile
the coarsely separated background and anomaly response map is utilized to guide the locally collaborative representation between pixels. Purer background characteristics can be expressed with the guidance of the rough detection map
and the suppression of anomalous pixels due to the polluted background in the detection process is avoided. Accordingly
the detection accuracy is improved. Meanwhile
parameters in the collaborative representation process decrease with the reduction in participating elements. Over-fitting phenomenon is unlikely to be produced with the simpler optimization model
which ensures the effectiveness of the algorithm. In this way
the proposed method utilizes not only the spectral characteristics of hyperspectral images but also their spatial characteristics and the prior information of spatial information. Experimental results and comparative analysis demonstrate the effectiveness of the proposed method in anomaly detection.
Altmann Y, Dobigeon N and Tourneret J Y. 2013. Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model. IEEE Transactions on Image Processing, 22(4): 1267-1276[DOI: 10.1109/TIP.2012.2210235]
Borghys D, Kåsen I, Achard V and Perneel C. 2012. Comparative evaluation of hyperspectral anomaly detectors in different types of background//Proceedings of the Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, USA: SPIE: 803-814[ DOI: 10.1117/12.920387 http://dx.doi.org/10.1117/12.920387 ]
Chang C I and Chiang 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]
Du B and Zhang L P. 2011. Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 49(5): 1578-1589[DOI: 10.1109/TGRS.2010.2081677]
He L, Li J, Liu C Y and Li S T. 2018. Recent advances on spectral-spatial hyperspectral image classification: an overview and new guidelines. IEEE Transactions on Geoscience and Remote Sensing, 56(3): 1579-1597[DOI: 10.1109/TGRS.2017.2765364]
Hosseiny B and Shah-Hosseini R. 2020. A hyperspectral anomaly detection framework based on segmentation and convolutional neural network algorithms. International Journal of Remote Sensing, 41(18): 6946-6975[DOI: 10.1080/01431161.2020.1752413]
Huang Z H, Kang X D, Li S T and Hao Q B. 2020. Game theory-based hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58(4): 2965-2976[DOI: 10.1109/TGRS.2019.2958359]
Huang Z H and Li S T. 2019. From difference to similarity: a manifold ranking-based hyperspectral anomaly detection framework. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 8118-8130[DOI: 10.1109/TGRS.2019.2918342]
Jablonski J A, Bihl T J and Bauer K W. 2015. Principal component reconstruction error for hyperspectral anomaly detection. IEEE Geoscience and Remote Sensing Letters, 12(8): 1725-1729[DOI: 10.1109/LGRS.2015.2421813]
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]
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]
Li L, Li W, Du Q and Tao R. 2020. Low-rank and sparse decomposition with mixture of Gaussian for hyperspectral anomaly detection. IEEE Transactions on Cybernetics: #2968750[DOI: 10.1109/TCYB.2020.2968750]
Li W and 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]
Li Y S, Hu J, Zhao X, Xie W Y and Li J J. 2017. Hyperspectral image super-resolution using deep convolutional neural network. Neurocomputing, 266: 29-41[DOI: 10.1016/j.neucom.2017.05.024]
Matteoli S, Diani M and Corsini G. 2010. A tutorial overview of anomaly detection in hyperspectral images. IEEE Aerospace and Electronic Systems Magazine, 25(7): 5-28[DOI: 10.1109/MAES.2010.5546306]
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]
Prasad S and Bruce L M. 2008. Decision fusion with confidence-based weight assignment for hyperspectral target recognition. IEEE Transactions on Geoscience and Remote Sensing, 46(5): 1448-1456[DOI: 10.1109/TGRS.2008.916207]
Qu Y, Wang W, Guo R, Ayhan B, Kwan C, Vance S and Qi H R. 2018. Hyperspectral anomaly detection through spectral unmixing and dictionary-based low-rank decomposition. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4391-4405[DOI: 10.1109/TGRS.2018.2818159]
Ranney K I and Soumekh M. 2006. Hyperspectral anomaly detection within the signal subspace. IEEE Geoscience and Remote Sensing Letters, 3(3): 312-316[DOI: 10.1109/LGRS.2006.870833]
Reed I S and Yu X L. 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]
So A M C, Jain P, Ma W K and Scutari G. 2020. Nonconvex optimization for signal processing and machine learning. IEEE Signal Processing Magazine, 37(5): 15-17[DOI: 10.1109/MSP.2020.3004217]
Tao R, Zhao X D, Li W, Li H C and Du Q. 2019. Hyperspectral anomaly detection by fractional Fourier entropy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(12): 4920-4929[DOI: 10.1109/JSTARS.2019.2940278]
van der Meer F D, van der Werff H M A, van Ruitenbeek F J A, Hecker C A, Bakker W H, Noomen M F, van der Meijde M, Carranza E J M, de Smeth J B and Woldai T. 2012. Multi-and hyperspectral geologic remote sensing: a review. International Journal of Applied Earth Observation and Geoinformation, 14(1): 112-128[DOI: 10.1016/j.jag.2011.08.002]
Wang J X, Xia Y and Zhang Y N. 2021. Anomaly detection of hyperspectral image via tensor completion. IEEE Geoscience and Remote Sensing Letters, 18(6): 1099-1103[DOI: 10.1109/LGRS.2020.2993214]
Xiang P, Zhou H X, Li H, Song S Z, Tan W, Song J L Q and Gu L. 2020. Hyperspectral anomaly detection by local joint subspace process and support vector machine. International Journal of Remote Sensing, 41(10): 3798-3819[DOI: 10.1080/01431161.2019.1708504]
Xie W Y, Li Y S, Lei J, Yang J, Chang C I and Li Z. 2020a. Hyperspectral band selection for spectral-spatial anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58(5): 3426-3436[DOI: 10.1109/TGRS.2019.2956159]
Xie W Y, Liu B Z, Li Y S, Lei J and Du Q. 2020b. Autoencoder and adversarial-learning-based semisupervised background estimation for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58(8): 5416-5427[DOI: 10.1109/TGRS.2020.2965995]
Xu Y, Wu Z B, Chanussot J and Wei Z H. 2018. Joint reconstruction and anomalydetection from compressive hyperspectral images using mahalanobis distance-regularized tensor RPCA. IEEE Transactions on Geoscience and Remote Sensing, 56(5): 2919-2930[DOI: 10.1109/TGRS.2017.2786718]
Xu Y C, Du B, Zhang L P and Chang S Z. 2020. A low-rank and sparse matrix decomposition- based dictionary reconstruction and anomaly extraction framework for hyperspectral anomaly detection. IEEE Geoscience and Remote Sensing Letters, 17(7): 1248-1252[DOI: 10.1109/LGRS.2019.2943861]
Yang Y X, Zhang J Q, Song S Z, Zhang C and Liu D L. 2020. Low-rank and sparse matrix decomposition with orthogonal subspace projection-based background suppression for hyperspectral anomaly detection. IEEE Geoscience and Remote Sensing Letters, 17(8): 1378-1382[DOI: 10.1109/LGRS.2019.2948675]
Zhang B, Zhao L and Zhang X L. 2020. Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images. Remote Sensing of Environment, 247: #111938[DOI: 10.1016/j.rse.2020.111938]
Zhang L L and Zhao C H. 2017. Tensor decomposition-based sparsity divergence index for hyperspectral anomaly detection. Journal of the Optical Society of America A, 34(9): 1585-1594[DOI: 10.1364/JOSAA.34.001585]
Zhang X, Wen G J and Dai W. 2016. A tensor decomposition-based anomaly detection algorithm for hyperspectral image. IEEE Transactions on Geoscience and Remote Sensing, 54(10): 5801-5820[DOI: 10.1109/TGRS.2016.2572400]
Zhao C H, Li C, Yao X F and Li W. 2020. Real-time kernel collaborative representation-based anomaly detection for hyperspectral imagery. Infrared Physics and Technology, 107: #103325[DOI:10.1016/j.infrared.2020.103325]
相关作者
相关机构
京公网安备11010802024621