# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications

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## Abstract

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## 1. Introduction

## 2. Part A: Bibliometric Analysis

**RQ 1**. What is the trend among scientific publications on hyperspectral image processing for anomaly detection in remote sensing applications?**RQ 2**. What are future research directions in this scientific field?

**IC 1**. The search string (TITLE-ABS-KEY (hyperspectral AND anomaly AND detection))**IC 2**. The publications are written in English.

**EC 1**. Reviews and conference reviews, books and book chapters, letters and notes.**EC 2**. Publications with less than three citations per year.

#### 2.1. Descriptive Bibliometric Analysis

#### 2.2. Authors Analysis

Document | Reference | Global Citations | Local Citations |
---|---|---|---|

Stein, D.W.J.; Beaven, S.G.; Hoff, L.E.; Winter, E.M.; Schaum, A.P.; Stocker, A.D. Anomaly detection from hyperspectral imagery. IEEE Signal Process Mag 2002, 19, 58–69, doi:10.1109/79.974730 | [15] | 554 | 54 |

Kwon, H.; Nasrabadi, N.M. Kernel RX-algorithm: A non-linear anomaly detector for hyperspectral imagery. IEEE Trans Geosci Remote Sens 2005, 43, 388–397, doi:10.1109/TGRS.2004.841487 | [26] | 470 | 56 |

Chang, C.I.; Chiang, S.S. Anomaly detection and classification for hyperspectral imagery. IEEE Trans Geosci Remote Sens 2002, 40, 1314–1325, doi:10.1109/TGRS.2002.800280 | [28] | 386 | 51 |

Ren, H.; Chang, C., I. Automatic spectral target recognition in hyperspectral imagery. IEEE Trans. Aerosp. Electron. Syst. 2003, 39, 1232–1249, doi:10.1109/TAES.2003.1261124. | [29] | 360 | 5 |

Matteoli, S.; Diani, M.; Corsini, G. A tutorial overview of anomaly detection in hyperspectral images. IEEE Aerosp Electron Syst Mag 2010, 25, 5–27, doi:10.1109/MAES.2010.5546306. | [11] | 322 | 33 |

Du, Q.; Fowler, J.E. Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis. IEEE Geoscience and Remote Sensing Letters 2007, 4, 201–205, doi:10.1109/LGRS.2006.888109. | [30] | 321 | 2 |

Banerjee, A.; Burlina, P.; Diehl, C. A support vector method for anomaly detection in hyperspectral imagery. IEEE Trans Geosci Remote Sens 2006, 44, 2282–2291, doi:10.1109/tgrs.2006.873019. | [31] | 286 | 38 |

Li, W.; Du, Q. Collaborative representation for hyperspectral anomaly detection. IEEE Trans Geosci Remote Sens 2015, 53, 1463–1474, doi:10.1109/tgrs.2014.2343955. | [27] | 251 | 42 |

Penna, B.; Tillo, T.; Magli, E.; Olmo, G. Transform Coding Techniques for Lossy Hyperspectral Data Compression. IEEE Trans Geosci Remote Sens 2007, 45, 1408–1421, doi:10.1109/TGRS.2007.894565. | [32] | 241 | 3 |

Du, B.; Zhang, L. A Discriminative Metric Learning Based Anomaly Detection Method. IEEE Trans Geosci Remote Sens 2014, 52, 6844–6857, doi:10.1109/TGRS.2014.2303895. | [33] | 220 | 27 |

**Table 5.**The top 10 documents with the highest number of local citations that were not selected within the applied search strategy (Figure 1).

Document | Reference | Local Citations |
---|---|---|

Reed, I.S.; Yu, X. Adaptive Multiple-Band CFAR Detection of an Optical Pattern with Unknown Spectral Distribution. IEEE Trans. Acoust. Speech Sign. Proces. 1990, 38, 1760–1770, doi:10.1109/29.60107. | [34] | 90 |

Carlotto, M.J. A cluster-based approach for detecting man-made objects and changes in imagery. IEEE Trans Geosci Remote Sens 2005, 43, 374–387, doi:10.1109/TGRS.2004.841481 | [35] | 32 |

Manolakis, D.; Shaw, G. Detection algorithms for hyperspectral imaging applications. IEEE Signal Process Mag 2002, 19, 29–43, doi:10.1109/79.974724. | [9] | 32 |

Nasrabadi, N.M. Hyperspectral target detection: An overview of current and future challenges. IEEE Signal Process Mag 2014, 31, 34–44, doi:10.1109/MSP.2013.2278992 | [18] | 21 |

Harsanyi, J.C.; Chang, C.I. Hyperspectral Image Classification and Dimensionality Reduction: An Orthogonal Subspace Projection Approach. IEEE Trans Geosci Remote Sens 1994, 32, 779–785, doi:10.1109/36.298007 | [36] | 19 |

Kerekes, J. Receiver operating characteristic curve confidence intervals and regions. IEEE Geoscience and Remote Sensing Letters 2008, 5, 251–255, doi:10.1109/lgrs.2008.915928. | [37] | 17 |

Manolakis, D.; Marden, D.; Shaw, G.A. Hyperspectral Image Processing for Automatic Target Detection Applications. Lincoln laboratory journal 2003, 14, 79–116 | [38] | 16 |

Nasrabadi, N.M. Regularization for spectral matched filter and RX anomaly detector. In Proceedings of Proc SPIE Int Soc Opt Eng, 2008 | [39] | 16 |

Chen, Y.; Nasrabadi, N.M.; Tran, T.D. Sparse Representation for Target Detection in Hyperspectral Imagery. IEEE J. Sel. Top. Signal Process. 2011, 5, 629–640, doi:10.1109/jstsp.2011.2113170 | [40] | 16 |

Chen, Y.; Nasrabadi, N.M.; Tran, T.D. Hyperspectral Image Classification Using Dictionary-Based Sparse Representation. IEEE Trans Geosci Remote Sens 2011, 49, 3973–3985, doi:10.1109/tgrs.2011.2129595. | [41] | 16 |

## 3. Part B: An Overview of Hyperspectral Image Processing for Anomaly Detection in Remote Sensing Applications

## 4. Mathematical Framework for Anomaly Detection

## 5. Unstructured Background Models

#### 5.1. Reed-Xiaoli (RX) Algorithm

#### Improved Variants of the RX Detector

#### 5.2. Nearest Neighbor Detectors

#### 5.3. Kernel-Based Models

#### 5.3.1. Kernel RX detector

#### 5.3.2. Kernel Density Estimate of the Background Distribution Models

#### 5.3.3. Support Vector Data Description (SVDD)

## 6. Structured Background Models

#### 6.1. Subspace Models

#### 6.1.1. Orthogonal Subspace Models

#### 6.1.2. Signal Subspace Models

#### 6.2. Cluster or Mixture-based Models

#### 6.2.1. Gaussian-Mixture Model

#### 6.2.2. Cluster or Segmentation Based Models

#### 6.3. Representation-based Models

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**(

**a**) Source trends for top 4 journals by number of documents (Table 2). (

**b**) Trends in scientific production depicted by the number of articles and average citations per year.

**Figure 3.**Scientific production of top 10 authors in the field over the time span of the research, determined and sorted by number of articles (N. articles) and total number of citations per year (TC per year). The figure was created using the bibliometrix R-package [23].

**Figure 4.**The development of the theme described by the title keywords. The graph represents the use of the most frequent title keywords (their frequencies are expressed on a logarithmic scale) over time. The figure was created using the bibliometrix R-package [23].

**Figure 5.**Word cloud generated from the most frequent keywords found in the titles of the analyzed documents. The size of the word is proportional to its frequency. The figure was created using the bibliometrix R-package [23].

**Figure 6.**Geometrical principle of local RX detector with three specific sliding windows: guard window and windows for calculation of $\widehat{\underset{\_}{\mu}}$ and $\underset{\_}{\underset{\_}{\widehat{\Gamma}}}$, with their respective widths ${w}_{g}$, ${w}_{\widehat{\underset{\_}{\mu}}}$ and ${w}_{\underset{\_}{\underset{\_}{\widehat{\Gamma}}}}$. Detection statistic is calculated in a convolutional manner using the squared Mahalanobis distance.

**Table 1.**Summary bibliometric statistics of the relevant publications on hyperspectral image processing for anomaly detection in remote sensing applications acquired by the presented search strategy (Figure 1).

Main Information | Result |

Time span | 2000–2020 |

Sources | 41 |

Total number of documents | 133 |

Average years from publication | 7.41 |

Average citations per documents | 72.65 |

Average citations per year per doc | 8.14 |

References | 4276 |

Document types | |

Article | 118 |

Conference paper | 15 |

Authors and collaboration | |

Authors | 299 |

Authors of single-authored documents | 5 |

Authors of multi-authored documents | 294 |

Co-Authors per Documents | 3.71 |

Collaboration Index | 2.3 |

**Table 2.**The most relevant sources (first two zones by Bradford’s law [25]), sorted in descending order by the number of documents.

Rank | Source Name | Documents | Zone ^{1} |
---|---|---|---|

1 | IEEE Transactions On Geoscience And Remote Sensing | 39 | 1 |

2 | IEEE Geoscience And Remote Sensing Letters | 15 | 1 |

3 | IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing | 15 | 2 |

4 | Remote Sensing | 9 | 2 |

5 | Proceedings Of SPIE - The International Society For Optical Engineering | 5 | 2 |

6 | Remote Sensing Of Environment | 3 | 2 |

7 | Eurasip Journal On Advances In Signal Processing | 2 | 2 |

8 | IEEE Access | 2 | 2 |

^{1}By Bradford’s law [25].

**Table 3.**The statistics of the 20 most relevant authors in the topic, sorted by total number of citations.

Author | SCOPUS Author ID | H-index | Total Citations | Number of Publications | First Publication (Year) |
---|---|---|---|---|---|

Chang CI | 35253647700 | 10 | 1259 | 10 | 2001 |

Du Q | 7202060063 | 11 | 1044 | 12 | 2007 |

Zhang L | 8359720900 | 13 | 962 | 13 | 2011 |

Du B | 55020400300 | 9 | 799 | 9 | 2011 |

Nasrabadi NM | 7006312852 | 3 | 724 | 3 | 2003 |

Stocker AD | 7006884172 | 2 | 698 | 2 | 2002 |

Kwon H | 7401838362 | 3 | 611 | 3 | 2003 |

Diani M | 7003735775 | 6 | 597 | 6 | 2010 |

Matteoli S | 24076749300 | 6 | 597 | 6 | 2010 |

Beaven SG | 57206689538 | 1 | 554 | 1 | 2002 |

Hoff LE | 7005107977 | 1 | 554 | 1 | 2002 |

Schaum AP | 57207501822 | 1 | 554 | 1 | 2002 |

Stein DWJ | 7401616297 | 1 | 554 | 1 | 2002 |

Winter EM | 7102040936 | 1 | 554 | 1 | 2002 |

Fowler JE | 7402370679 | 4 | 513 | 4 | 2007 |

Chiang SS | 7201472110 | 2 | 511 | 2 | 2001 |

Li J | 24481713500 | 5 | 496 | 5 | 2014 |

Corsini G | 7103074007 | 5 | 486 | 5 | 2010 |

Li W | 56215159000 | 4 | 442 | 4 | 2015 |

Plaza A | 7006613644 | 5 | 420 | 5 | 2010 |

**Table 6.**The topic development expressed by the key author’s keywords over time. The most relevant keywords were filtered and manually selected by authors.

Year | Document |
---|---|

2001 | Competitive Region Growth, Elliptically Contoured Distributions, Evolutional Algorithm, Kurtosis, Projection Pursuit, Spherically Invariant Random Vectors |

2002 | Causal RXD, Correlation Matched-Filter-Based Measure, Target Discrimination Measure |

2003 | Clustering Algorithms, Dual Window, Eigen Separation Transform, Embedded Computing |

2005 | Kernels, Linear Discriminant Analysis, Orthogonal Subspace Projection/AD, RX Detector, Signal Parameter Estimation |

2006 | Bhattacharyya Distance, Signal Subspace Processing, Support Vector Data Description (SVDD) |

2007 | Detection Index, Minimum Description Length, Real-Time (R-T) Processing, Self-Organising Maps, Separability Index, Signal-Subspace Rank, Singular Value Decomposition, Wavelets |

2008 | Karhunen-Love-Transform, Principal Component Analysis (PCA), Kernel PCA, Signal Detection, Spectral Decorrelation |

2009 | GPU Processing, Generalized Least Squares, Maximum Autocorrelation Factors, Multivariate Normal Mixture Model, Principal Autocorrelation Factors |

2010 | Cluster-Based Approach, Feature Selection, Kernel-Based Learning, Quasi-Local Covariance Matrix, Regularization, Robust Locally Linear Embedding |

2011 | Embedded Systems, Gaussian Kernel, Independent Component Analysis, ROC Space, Sparse Matrix Transform, Support vector machine (SVM) |

2012 | Clustering, Compressed Sensing, PCA, Segmentation-Based AD, Sparse Kernel-Based Ensemble Learning, Spectral Unmixing |

2013 | Bayesian Learning, Dual Window-Based Eigen Separation Transform, Finite Mixture Model, Kernel density estimation (KDE), Multicore Platforms, Multiple-Window AD, Nonlinear PCA |

2014 | Dimensionality Reduction, High-Order Statistics, Local Sparsity Divergence, Low-Rank (L-R) And Sparse, Matched Filter, Robust Regression Analysis, Superpixels, Variable Bandwidth KDE, Weighted-RXD |

2015 | Graph Theory, High Order Statistics, L-R Approximation, Manifold Learning, R-T processing, Residual Analysis, Robust Background Estimation |

2016 | Cluster Kernel RX, Dual Clustering, Kernel Collaborative Representation (CR), Local Summation Strategy, Locally Linear Embedding, ROC, Robust PCA, Sparse Representation (SR), Sparsity Divergence Index, Spectral-Spatial Integration, Tensor Representation |

2017 | 3-D ROC, Band Subset Selection, Convolutional Neural Network (NN), Differential Morphology, Edge-Preserving Filtering, Joint SR, K-SVD, Multiple Graphs, Autoencoders, Tensor Decomposition |

2018 | A Posteriori AD, Band Selection, Deep Learning, Feature Extraction, Inverse PCA, Iterative AD, L-R Representation, Multiple Dictionaries, R-T Applications, Sparse Coding, Structured SR |

2019 | Adaptive Weighting, Constrained SR, Deep Brief Network, Dictionary Learning, Fractional Fourier, Local Summation, Low Dimensional Manifold Model, Structure Tensor |

2020 | Density Peak Clustering, Isolation Forest, Radiative Transfer Modeling |

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Racetin, I.; Krtalić, A.
Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications. *Appl. Sci.* **2021**, *11*, 4878.
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Racetin I, Krtalić A.
Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications. *Applied Sciences*. 2021; 11(11):4878.
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Racetin, Ivan, and Andrija Krtalić.
2021. "Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications" *Applied Sciences* 11, no. 11: 4878.
https://doi.org/10.3390/app11114878