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3D Gabor Based Hyperspectral Anomaly Detection | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 11، دوره 50، شماره 2، اسفند 2018، صفحه 189-194 اصل مقاله (682.2 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2018.14958.5117 | ||
| نویسنده | ||
| M. Imani* | ||
| Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran | ||
| چکیده | ||
| Hyperspectral anomaly detection is one of the main challenging topics in both military and civilian fields. The spectral information contained in a hyperspectral cube provides a high ability for anomaly detection. In addition, the costly spatial information of adjacent pixels such as texture can also improve the discrimination between anomalous targets and background. Most studies miss the worthful spatial characteristics. Moreover, some works that include the spatial features in the anomaly detection process extract features from each hyperspectral band that is a two dimensional image while the original structure of hyperspectral cube contains three dimensions. Ignoring the nature of hyperspectral image leads to lose the contained spectral-spatial correlations in the hyperspectral cube. To deal with this difficulty, in this work, the fused spectral and spatial features obtained by applying 3D Gabor filters are proposed for hyperspectral anomaly detection. Exploiting the 3D structure of hyperspectral cube by capturing multiple scales, orientations and its spectral-dependent characteristics provides an appropriate spectral-spatial feature space for anomalous targets detection. The extracted features are applied to the regularized RX detector to provide the detection map. The experiments show the superior performance of the proposed Gabor 3D based detector in terms of detection accuracy and computation time. | ||
| کلیدواژه‌ها | ||
| Anomaly detection؛ 3D Gabor filter؛ RX detector؛ Hyperspectral image | ||
| مراجع | ||
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[1] Y. Niu, B. Wang, Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary, IEEE Transactions on Geoscience and Remote Sensing, 55(3) (2017) 1604-1617. [2] M. Imani, Attribute profile based target detection using collaborative and sparse representation, Neurocomputing, 313 (2018) 364-376. [3] M. Diani, M. Moscadelli, G. Corsini, Improved Alpha Residuals for Target Detection in Thermal Hyperspectral Imaging, IEEE Geoscience and Remote Sensing Letters, 15(5) (2018) 779-783. [4] E. Lo, Hyperspectral anomaly detection based on a generalization of the maximized subspace model, 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Gainesville, FL, 2013, pp. 1-5. [5] L. Lee, D. Paylor, C. Chang, Anomaly discrimination and classification for hyperspectral imagery, 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, 2015, pp. 1-4. [6] M. Imani, Hyperspectral Anomaly Detection Using Differential Image, IET Image Processing, 12(5) (2018) 801-809. [7] S. Reed, X. Yu, Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution, IEEE Transactions on Acoustics, Speech, and Signal Processing, 38 (10) (1990) 1760–1770. [8] N. M. Nasrabadi, Regularization for spectral matched filter and RX anomaly detector, Proc. SPIE, 6966 (696604) (2008). [9] M. Imani, Anomaly detection using morphology-based collaborative representation in hyperspectral imagery, European Journal of Remote Sensing, 51(1) (2018) 457–471. [10] M. Imani, H. Ghassemian, Weighted Joint Collaborative Representation Based On Median-Mean Line and Angular Separation, IEEE Transactions on Geoscience and Remote Sensing, 55(10) (2017) 5612 – 5624. [11] J. López-Fandiño, D. B. Heras, F. Argüello, R. J. Duro, CUDA multiclass change detection for remote sensing hyperspectral images using extended morphological profiles, 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Bucharest, 2017, pp. 404-409. [12] T. Viarbitskaya, A. Dobrucki, Elimination of distortions in the recorded sound of the violin using cascade-integrator-comb filters based on Gabor transform and pitch-shifting algorithm, 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), Poznan, 2016, pp. 56-61. [13] L. Tao, H. K. Kwan, Fast Parallel Approach for 2-D DHT-Based Real-Valued Discrete Gabor Transform, IEEE Transactions on Image Processing, 18(12) (2009) 2790-2796. [14] M. Imani, H. Ghassemian, GLCM, Gabor, and Morphology Profiles Fusion for Hyperspectral Image Classification, IEEE proceedings of the 24th Iranian Conference on Electrical Engineering (ICEE 2016), Shiraz, Iran, 2016, pp. 460-465. [15] W. Li, Q. Du, Gabor-Filtering-Based Nearest Regularized Subspace for Hyperspectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4) (2014) 1012-1022. [16] L. He, J. Li, A. Plaza, Y. Li, Discriminative Low-Rank Gabor Filtering for Spectral–Spatial Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 55 (3) (2017) 1381-1395. [17] S. Jia, L. Shen, J. Zhu, Q. Li, A 3-D Gabor Phase-Based Coding and Matching Framework for Hyperspectral Imagery Classification, IEEE Transactions on Cybernetics, 48(4) (2018) 1176-1188. [18] T. C. Bau, S. Sarkar, G. Healey, Hyperspectral Region Classification Using a Three-Dimensional Gabor Filterbank, IEEE Transactions on Geoscience and Remote Sensing, 48(9) (2010) 3457-3464. [19] M. Imani, RX Anomaly Detector with Rectified Background, IEEE Geoscience and Remote Sensing Letters, 14(8) (2017) 1313-1317. | ||
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