Single-Spectrum-Driven Binary-Class Sparse Representation Target Detector for Hyperspectral Imagery

In this article, a single-spectrum-driven binary-class sparse representation target detector (SSBSTD) via target and background dictionary construction (BDC) is proposed. The SSBSTD leans upon the binary-class sparse representation (BSR) model. Due to the fact that a background spectrum usually consists in background samples composed low-dimensional subspace and a target spectrum also consists in target samples composed low-dimensional subspace, only background samples should be used for sparsely representing the test pixel under the target absent hypothesis and the samples from target-only dictionary for target present hypothesis. To alleviate the problem that there are insufficient available target samples in the sparse representation model, this article proposed a predetection method to construct the target dictionary utilizing the given target spectrum. With regard to the BDC, we proposed an approach based on the classification to generate a global over-complete background dictionary. The detection output is composed of the residual difference between the BSR. Extensive experiments were made on four benchmark hyperspectral images and the experimental results indicate that our SSBSTD algorithm demonstrates superior detection performances.

[1]  Stefania Matteoli,et al.  An Automatic Approach to Adaptive Local Background Estimation and Suppression in Hyperspectral Target Detection , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Xiangtao Zheng,et al.  Spectral–Spatial Attention Network for Hyperspectral Image Classification , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Bo Du,et al.  Target Dictionary Construction-Based Sparse Representation Hyperspectral Target Detection Methods , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Qian Du,et al.  Combined sparse and collaborative representation for hyperspectral target detection , 2015, Pattern Recognit..

[5]  Wei An,et al.  A Constrained Sparse Representation Model for Hyperspectral Anomaly Detection , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Chein-I Chang,et al.  Generalized constrained energy minimization approach to subpixel target detection for multispectral imagery , 2000 .

[7]  Louis L. Scharf,et al.  The CFAR adaptive subspace detector is a scale-invariant GLRT , 1999, IEEE Trans. Signal Process..

[8]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[9]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[10]  Bo Du,et al.  Beyond the Sparsity-Based Target Detector: A Hybrid Sparsity and Statistics-Based Detector for Hyperspectral Images , 2016, IEEE Transactions on Image Processing.

[11]  Xiaoqiang Lu,et al.  Exploiting Embedding Manifold of Autoencoders for Hyperspectral Anomaly Detection , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Bo Du,et al.  IBRS: An Iterative Background Reconstruction and Suppression Framework for Hyperspectral Target Detection , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Licheng Jiao,et al.  Hyperspectral Anomaly Detection via Background and Potential Anomaly Dictionaries Construction , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Heesung Kwon,et al.  Dual-window-based anomaly detection for hyperspectral imagery , 2003, SPIE Defense + Commercial Sensing.

[15]  I. Reed,et al.  Rapid Convergence Rate in Adaptive Arrays , 1974, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Chein-I Chang,et al.  Orthogonal subspace projection (OSP) revisited: a comprehensive study and analysis , 2005, IEEE Trans. Geosci. Remote. Sens..

[17]  Xuelong Li,et al.  Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Bo Du,et al.  A Sparse Representation-Based Binary Hypothesis Model for Target Detection in Hyperspectral Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Trac D. Tran,et al.  Simultaneous Joint Sparsity Model for Target Detection in Hyperspectral Imagery , 2011, IEEE Geoscience and Remote Sensing Letters.

[20]  Louis L. Scharf,et al.  Matched subspace detectors , 1994, IEEE Trans. Signal Process..

[21]  Bo Du,et al.  A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Qian Du,et al.  A signal-decomposed and interference-annihilated approach to hyperspectral target detection , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Daniel R. Fuhrmann,et al.  A CFAR adaptive matched filter detector , 1992 .

[24]  Xindong Wu,et al.  Manifold elastic net: a unified framework for sparse dimension reduction , 2010, Data Mining and Knowledge Discovery.

[25]  Liangpei Zhang,et al.  Sparse Transfer Manifold Embedding for Hyperspectral Target Detection , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Bo Du,et al.  BASO: A Background-Anomaly Component Projection and Separation Optimized Filter for Anomaly Detection in Hyperspectral Images , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Olgica Milenkovic,et al.  Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.

[28]  Ahmad W. Bitar,et al.  Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Stefania Matteoli,et al.  Hyperspectral Airborne “Viareggio 2013 Trial” Data Collection for Detection Algorithm Assessment , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Yulong Wang,et al.  Sparse Coding From a Bayesian Perspective , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Stephen J. Wright,et al.  Computational Methods for Sparse Solution of Linear Inverse Problems , 2010, Proceedings of the IEEE.

[32]  Bo Du,et al.  Maximum margin metric learning based target detection for hyperspectral images , 2015 .

[33]  Qian Du,et al.  Automated Target Detection and Discrimination Using Constrained Kurtosis Maximization , 2008, IEEE Geoscience and Remote Sensing Letters.

[34]  Dimitris G. Manolakis,et al.  Detection algorithms for hyperspectral imaging applications , 2002, IEEE Signal Process. Mag..

[35]  Xuelong Li,et al.  A Hybrid Sparsity and Distance-Based Discrimination Detector for Hyperspectral Images , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Hongsheng Zhang,et al.  A Sparse Representation Method for a Priori Target Signature Optimization in Hyperspectral Target Detection , 2018, IEEE Access.

[37]  Bo Du,et al.  Spatially Adaptive Sparse Representation for Target Detection in Hyperspectral Images , 2017, IEEE Geoscience and Remote Sensing Letters.

[38]  Nasser M. Nasrabadi,et al.  Regularized Spectral Matched Filter for Target Recognition in Hyperspectral Imagery , 2008, IEEE Signal Processing Letters.

[39]  Bo Du,et al.  Target detection based on a dynamic subspace , 2014, Pattern Recognit..

[40]  Bo Du,et al.  Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[42]  Eric Truslow,et al.  Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms , 2014, IEEE Signal Processing Magazine.

[43]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[44]  Gary A. Shaw,et al.  Hyperspectral Image Processing for Automatic Target Detection Applications , 2003 .

[45]  J. Hanley,et al.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases. , 1983, Radiology.

[46]  Qian Du,et al.  GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Trac D. Tran,et al.  Sparse Representation for Target Detection in Hyperspectral Imagery , 2011, IEEE Journal of Selected Topics in Signal Processing.

[48]  Nasser M. Nasrabadi,et al.  Hyperspectral Target Detection : An Overview of Current and Future Challenges , 2014, IEEE Signal Processing Magazine.

[49]  Bo Du,et al.  Hybrid Detectors Based on Selective Endmembers , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Qian Du,et al.  Real-time constrained linear discriminant analysis to target detection and classification in hyperspectral imagery , 2003, Pattern Recognit..

[51]  Qian Du,et al.  A comparative study for orthogonal subspace projection and constrained energy minimization , 2003, IEEE Trans. Geosci. Remote. Sens..