# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 2 Part A: Bibliometric Analysis
### Authors Analysis
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 bibliometric R-package [23]. Although the search strategy did not cover it, the paper published in 1990 by [PERSON] and [PERSON] [34] received by far the highest number of local citations. In that paper, the foundations of the well-known (Reed-Xiaoli, RX) hyperspectral anomaly detector were established. This algorithm is still popular; it is widely regarded as the benchmark hyperspectral anomaly detection technique and is often used in the comparison of newly developed anomaly detectors. [PERSON] and [PERSON] (Table 5) are among the pioneers of hyperspectral target and anomaly detection, and their work is also highly cited. The diligent work of [PERSON] has been recognized, accounting for as many as four out of the 10 top locally cited publications in Table 5. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Document** & **Reference** & **Global Citations** & **Local Citations** \\ \hline [PERSON]; [PERSON]; [PERSON]; [PERSON]; & & & \\ [PERSON]; [PERSON] Anomaly detection from hyperspectral imagery. IEEE Signal Process Mag 2002, 19, 58–69, doi:10.1109/79.974730 & [15] & 554 & 54 \\ \hline [PERSON]; [PERSON] 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 \\ \hline [PERSON]; [PERSON] 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 \\ \hline [PERSON]; [PERSON] 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 \\ \hline [PERSON]; [PERSON]; [PERSON] [PERSON] A tutorial overview of anomaly detection in hyperspectral images. IEEE Aerosp Electron Syst Mag 2010, 25, 5–27, doi:10.1109/TAES.2010.5546306. & [11] & 322 & 33 \\ \hline [PERSON]; [PERSON] 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 \\ \hline [PERSON]; [PERSON]; [PERSON] 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 \\ \hline [PERSON]; [PERSON] Collaborative representation for hyperspectral anomaly detection. IEEE Trans Geosci Remote Sens 2015, 53, 1463–1474, doi:10.1109/tgrs.2014.2343955. & [27] & 251 & 42 \\ \hline [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] 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 \\ \hline [PERSON]; [PERSON] 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 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The top 10 documents with the highest number of global citations. Figure 4 and Table 6 depict the temporal development of research on hyperspectral image processing for anomaly detection in remote sensing applications. Table 6 is based on the use of the author's keywords, and Figure 4, on the document title's keywords. In selecting the author's keywords presented in Table 6, trivial keywords such as hyperspectral, anomaly, detection, target, background, and similar terms were omitted. Then, the most relevant keywords were manually selected from the remaining keywords based on keyword frequency and global citation of the respective document. For the creation of Figure 4, the most frequent title keywords per year are displayed with the constraint of a maximum of three keywords per year.