# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 1 Introduction
Anomaly detection methods in hyperspectral images refer to a TD class that does not require any a-priori knowledge about a hyperspectral scene or target spectrum and, therefore, can be regarded as unsupervised learning techniques. Based on statistical techniques, models, or assumptions, unsupervised learning methods aim to find regularities in the input data [14]. A structure in the input space is presumed such that some patterns happen more than others, while some occur rarely. The goal of unsupervised learning is to discover and analyze these patterns. Hence, anomaly detection methods can be performed in reflectance, radiance, or any other units. Basically, anomaly detection methods try to model the image background, and pixels with a spectrum that does not fit the defined background model are then proclaimed as anomalies [9; 10; 11; 15]. Consequently, anomaly detectors are generally unable to distinguish detected targets between each other, nor can they judge whether the detected anomaly is just a rare pixel or a target of interest. However specific techniques, such as described in [16], have been developed to discriminate anomalies between each other. Desired characteristics of anomaly detectors can be identified: high probability of detection and low false alarm rate, repeatability of detection performance in different test scenarios and scenes with different levels of complexity, automatic determination of detection (test) statistic threshold, low computational complexity, ability to perform in a real-time, constant false alarm rate (CFAR) operation under the selected statistical model, and robustness of detection performance in regard to a parameter selection (detection performance is not significantly influenced by small changes in its parameters). A variety of approaches in background modeling have been developed on the basis of a general background model, a spatial model (global, local, or quasi-local), the ability to detect resolved (full pixel) or unresolved (sub-pixel) anomalies, detection statistic selection, and threshold determination. To the best of our knowledge, to date no review papers have been published on hyperspectral image processing for anomaly detection in remote sensing applications. However, there are papers that are formally classified as research papers but provide a good overview of the subject area, including [PERSON] [10], [PERSON], [PERSON], and [PERSON] [11], [PERSON], [PERSON], and [PERSON] [17], and [PERSON] [18]. Nevertheless, the most recent of these was written almost 10 years ago, and in the interim, some new approaches in hyperspectral anomaly detection have been developed. This paper is the first review dealing with hyperspectral image processing for anomaly detection in remote sensing applications. The paper is organized into two parts, A and B. The first part deals with the bibliometric analysis, and the second part, with a methodological overview of the field. The research in bibliometric analysis was conducted to determine the trends and future research challenges in this field. Part B contains the mathematical framework of anomaly detection methods used on hyperspectral images along with a presentation of relevant anomaly detection techniques.