# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 7 Conclusions  
In this review paper, the development of hyperspectral image processing for anomaly detection in remote sensing applications was presented. In the first part of the paper, scientific research trends were presented through a bibliometric analysis. The most relevant journals, authors, and their contributions were identified, and the expansion of the field was analyzed by title and author keywords. Although the documents used in this research were mostly published in the last 20 years, as the hyperspectral imaging technology is quite recent, the oldest references date back to the 1930s. This analysis provided the foundations for the second part of the paper, in which the overview of the mathematical framework for anomaly detection on hyperspectral images was presented. Developed anomaly detection techniques were generally classified as the methods that presume an unstructured background model or, conversely, a structured background model. The latter assumes a specific background structure, while the former method does not state any a-priori assumptions about it. A plethora of innovative concepts and ideas were applied to the anomaly detection problem on hyperspectral images: generative approaches, nonlinear mapping and kernel trick, orthogonal and signal subspace projections, and representation approaches such as sparse, collaborative, or joint ones. No doubt, every one of these approaches has positive and negative sides that could make one detector excel in some specific scenarios, but unfortunately, might not maintain the same detection performance if the application circumstances change. There is debate as to whether the best hyperspectral anomaly detector in remote sensing applications exists, but no such statement can be made. Regardless of the underlying concept, the main problem in anomaly detection performance assessment arises from the fact that the detectors are judged on the basis of a small number of experimental scenarios. The scarcity of reference hyperspectral datasets for detection performance evaluation is still an issue. A rich collection of hyperspectral images of various natural scenes would contribute more statistical significance to the comparative results of anomaly detector performance. The specific problems in evaluating detector performance come from the sole nature of the hyperspectral images of natural scenes: they reside in highly dimensional spectral space that exhibits high spatial non-stationarity. Due to its high dimensionality, hyperspectral data processing delivers a heavy computational burden. Hence, many authors have tackled the problem of reducing or optimizing the computational complexity and real-time processing of hyperspectral images for detection purposes. Real-time processing may refer to processing the data in an online fashion or the ability to deliver detection results in real-time. These issues are still open and have attracted the scientific community's attention, as shown in recent publications focused on representation-based techniques and the implementation of neural network based approaches [163; 164]. Future research in the field may continue to develop these approaches and techniques, as they offer a balance between detection performance and computational complexity. Nevertheless, hyperspectral image processing for anomaly detection in remote sensing applications is still an exceptionally worthy field of research, as the hyperspectral data carry an abundance of valuable information that may be useful in a wide variety of applications. Conceptualization, I.R. and A.K. ; methodology, I.R. and A.K. ; formal analysis, I.R. ; writing--original draft preparation, I.R. ; writing--review and editing, A.K. ; visualization, I.R. ; supervision, A.K. ; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the European Community's Seventh Framework Programme (FP7-SECURITY-Specific Programme \"Cooperation\": Security), under grant agreement No. [28474] (\"TIRAMISU project\").