# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 2 Part A: Bibliometric Analysis  
The main goal of the bibliometric analysis performed in this research was to answer the following research questions (RQ):  
* **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?  
Bibliographic databases were analyzed in this research, resulting in the selection of the Web of Science (Web of Knowledge, [[https://apps.webofknowledge.com](https://apps.webofknowledge.com)]([https://apps.webofknowledge.com](https://apps.webofknowledge.com)) (accessed on 24 April 2021)) and Scopus ([[https://www.scopus.com/](https://www.scopus.com/)]([https://www.scopus.com/](https://www.scopus.com/)) (accessed on 24 April 2021)) databases [19; 20] as the most relevant for this research topic. Using the same search strategy and criteria, Web of Science found 725 documents, while the Scopus database provided 973 documents. Hence, the results of the Scopus bibliographic database were used for subsequent analysis. According to the Scopus Content Coverage Guide [21] data from January 2020, it covers more than 5000 publishers and contains over 25,000 active titles with more than 77 million publications. The Scopus records date back to 1788, with over 6.6 million records published prior to 1970 [21]. The main focus of the SCOPUS database is classified in four subject clusters [21]: health sciences (30.4%), physical sciences (28.0%), social sciences (26.2%) and life sciences (15.4%).  
To select relevant publications on hyperspectral image processing for anomaly detection in remote sensing applications, the PRISMA methodology [22] for systematic reviews was followed. The search strategy was conceived around three keywords: hyperspectral, anomaly, and detection, which were interconnected with the conjunction AND. The chosen terms were searched in the publication titles, abstracts, or authors' keywords to compile an extensive document list. As the research was conducted in January 2021, it encompassed publications published up to the end of 2020. No exclusion criterion (EC) was defined for the research time span, as the goal of RQ 1 was to identify the research trend over the years. To emphasize, the manuscript selection was initiated with two inclusion criteria (IC):  
* **IC 1**. The search string (TITLE-ABS-KEY (hyperspectral AND anomaly AND detection))
* **IC 2**. The publications are written in English.  
The exclusion criteria (EC) define what publications should be discarded from the research collection. In this work, two EC were defined:  
* **EC 1**. Reviews and conference reviews, books and book chapters, letters and notes.
* **EC 2**. Publications with less than three citations per year.  
The first EC resulted in the selection of articles and conference papers that could be seen as the primary sources of scientific contributions, whereas the excluded document types might not have provided sufficiently novel material on the research subject. The second EC served as the relevance criterion, such that only highly-cited publications were selected for final bibliometric analysis. The overall procedure is depicted in Figure 1.  
The bibliometric analysis was performed in the programming language R, using the bibliometrix [23] package. Bibliometric is an open-source R-package that enables comprehensive science mapping analysis [23].