# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 2 Part A: Bibliometric Analysis
### Authors Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Document** \\ \hline
2001 & Competitive Region Growth, Elliptically Contoured Distributions, Evolutional Algorithm, Kurtosis, Projection \\ Pursuit, Spherically Invariant Random Vectors \\ \hline
2002 & Causal RXD, Correlation Matched-Filter-Based Measure, Target Discrimination Measure \\ \hline
2003 & Clustering Algorithms, Dual Window, Eigen Separation Transform, Embedded Computing \\ \hline
2005 & Kernels, Linear Discriminant Analysis, Orthogonal Subspace Projection/AD, RX Detector, \\ Signal Parameter Estimation \\ \hline
2006 & Bhattacharyya Distance, Signal Subspace Processing, Support Vector Data Description (SVDD) \\ \hline
2007 & Detection Index, Minimum Description Length, Real-Time (R-T) Processing, Self-Organising Maps, Separability \\ Index, Signal-Subspace Rank, Singular Value Decomposition, Wavelets \\ \hline
2008 & Karhunen-Love-Transform, Principal Component Analysis (PCA), Kernel PCA, \\ Signal Detection, Spectral Decorrelation \\ \hline
2009 & GPU Processing, Generalized Least Squares, Maximum Autocorrelation Factors, Multivariate Normal Mixture \\ Model, Principal Autocorrelation Factors \\ \hline
2010 & Cluster-Based Approach, Feature Selection, Kernel-Based Learning, Quasi-Local Covariance Matrix, \\ Regularization, Robust Locally Linear Embedding \\ \hline
2011 & Embedded Systems, Gaussian Kernel, Independent Component Analysis, ROC Space, Sparse Matrix Transform, \\ Support vector machine (SVM) \\ \hline
2012 & Clustering, Compressed Sensing, PCA, Segmentation-Based AD, Sparse Kernel-Based Ensemble \\ Learning, Spectral Unmixing \\ \hline
2013 & Bayesian Learning, Dual Window-Based Eigen Separation Transform, Finite Mixture Model, Kernel density \\ estimation (KDE), Multicore Platforms, Multiple-Window AD, Nonlinear PCA \\ \hline
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 \\ \hline
2015 & Graph Theory, High Order Statistics, L-R Approximation, Manifold Learning, R-T processing, Residual Analysis, \\ Robust Background Estimation \\ \hline
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 \\ \hline \hline \end{tabular}
\end{table} 
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. 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 bibliometric R-package [23]. The Word cloud presented in Figure 5 enables clear insight into the trends and future scientific research in hyperspectral image processing for anomaly detection in remote sensing applications. The figure was generated using the document title keywords, similarly as in Figure 4, but here, the total frequency over the analyzed time span was considered. The total frequencies determined the respective size of words, so the most frequent keywords were written with more prominent characters. By analyzing Figures 4 and 5 and Table 6, a recent trend in the development of representation-based techniques can be identified. Low-rank, spare, collaboration, joint, and dictionary are terms that can be related to these techniques. Hence, a special section is dedicated to these methods in Part B of this paper.