Developing an algorithm for local anomaly detection based on spectral space window in hyperspectral image
Tóm tắt
A local anomaly detection algorithm based on sliding windows in spectral space has been proposed in this research. The traditional local anomaly detection algorithms are implemented in spatial windows because local data of an image scene is more suitable for a single statistical model than global data. However, from the aspect of geometric structure of a dataset, this assumption is not entirely proper. As multivariate data, the hyperspectral image dataset can be considered as a low-dimensional manifold, embedded in the high-dimensional spectral space. The nonlinear spectral mixture occurs more frequently, as well as a low dimensional manifold being nonlinear. The traditional spatial local anomaly detection algorithms based on linear projection would not be appropriate to deal with this kind of data. This paper studies the local linear ideas in manifold learning, and an anomaly detection algorithm has been implemented based on the linear projections in a local area of spectral space. The key concept is that a small neighborhood areas of nonlinear manifold can be considered as a local linear structure. The classic spatial local algorithms and proposed algorithm are compared by using real hyperspectral images from vehicle and aviation platforms. The results demonstrated the effectiveness of the proposed algorithm in improving detection of the weak anomalies that decreases the number of false alarms.
Tài liệu tham khảo
Agovic, A., A. Banerjee, A. Ganguly, V. Protopopescu, 2007, Anomaly Detection in Transportation Corridors using Manifold Embedding, San Jose, CA. http://www-users.cs.umn.edu/~aagovic/kdd-anomaly2.pdf
Ahlberg J, Renhorn I, Forskningsinstitut T (2004) Multi-and Hyperspectral Target and Anomaly Detection. Linköping, FOI, Sweden
Bachega, L.R., J. Theiler, C.A. Bouman, 2011, Evaluating and improving local hyperspectral anomaly detectors, Applied Imagery Pattern Recognition Workshop (AIPR), doi: 10.1109/AIPR.2011.6176369 IEEE, pp1-8
Barata J. C. A., Hussein M. S., 2012, The Moore–Penrose pseudoinverse: A tutorial review of the theory, Brazilian Journal of Physics, (42): 146–165, doi: 10.1007/s13538-011-0052-z
Borghys, D., I. Kasen, V. Achard, C. Perneel, 2012, Comparative evaluation of hyperspectral anomaly detectors in different types of background. Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Chang CI (2005) Orthogonal subspace projection (OSP) revisited: a comprehensive study and analysis. IEEE Trans Geosci Remote Sens 43(3):502–518
Chang CI, Chiang SS (2002) Anomaly detection and classification for hyperspectral imagery. IEEE Trans Geosci Remote Sens 40(6):1314–1325
Chao D, Huijie Z, Wei W (2009) Hyperpectral image anomaly detection based on local orthogonal subspace projection. Opt Precis Eng 17(8):2004–2010
Du B, Zhang L (2014a) A discriminative metric learning based anomaly detection method. IEEE Trans Geosci Remote Sens 52(11):6844–6857
Du B, Zhang L (2014b) Target detection based on a dynamic subspace. Pattern Recogn 47(1):344–358
Harsanyi, J. C., W. Farrand, and C.-I. Chang, 1994, Detection of subpixel signatures in hyperspectral image sequences. in Proc. Amer. Soc. Photogram. Remote Sens., Reno, NV, pp236-247
Huck A, Guillaume M (2010) Asymptotically CFAR-unsupervised target detection and discrimination in hyperspectral images with anomalous-component pursuit. IEEE Trans Geosci Remote Sens 48(11):3980–3991
Koppen, M., 2000, The curse of dimensionality, 5th Online World Conference on Soft Computing in Industrial Applications (WSC5), http://yaroslavvb.com/papers/koppen-curse.pdf
Li M, Crawford MM, Tian J (2010a) Anomaly detection for hyperspectral images based on robust locally linear embedding. Journal of Infrared, Millimeter, and Terahertz Waves 31(6):753–762
Li M., Crawford M M, Tian J, 2010b, Anomaly detection for hyperspectral images using local tangent space alignment, Geoscience and Remote Sensing Symposium (IGARSS), 2010 I.E. International, pp824-827
Li ZY, Wang LL, Zheng SY (2014) Applied low dimension linear manifold in hyperspectral imagery anomaly detection, Proc. SPIE 9142:91421–91429
Matteoli S, Diani M, Corsini G (2010) A tutorial overview of anomaly detection in hyperspectral images. IEEE A&E Systems Magazine 25(7):5–27
Messinger DW, Chester F (2011) A graph theoretic approach to anomaly detection in hyperspectral imagery, 3rd hyperspectral image and signal processing: evolution in remote sensing (WHISPERS). Lisbon, pp 1–4
Nasrabadi NM (2014) Hyperspectral target detection-an overview of current and future challenges. IEEE Signal Processing Magzine, pp 34–44
Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets, SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp 427–438
Reed IS, 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
Schaum, A., 2007, Hyperspectral anomaly detection: Beyond RX. Proc. SPIE6565 Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery XII, Paper on 656502
Seung H, Lee D (2000) The manifold ways of perception. Science 290(5500):2268–2269
Stein DWJ, Beaven SG, Lawrence Hoff E (2002) Anomaly detection from hyperspectral imagery. IEEE Signal Process Mag 19(1):58–69
Verveer, Peter j., Robert P. W. Duin, 1995, An Evaluation of Intrinsic Dimensionality Estimators, IEEE Trans. On Pattern Analysis and Machine Intelligence,Vol.17, NO.1, pp81-85
Zhao, M., V. Saligrama, 2009, Anomaly Detection with Score functions based on Nearest Neighbor Graphs, arXiv.org > cs > arXiv:0910.5461v1