Charting the Evolution: Bibliometric Perspectives on Anomaly Detection within Hyperspectral Domains

In this article the results of the disruption module in the hyperspectral image field are analyzed and present itself. The aim is to see the major concerns, techniques and potential areas that can be researched as topics in the bibliometric method. Highly specified hyper-spectral spectroscopy that can simultaneously analyze the spectral data beyond the visual spectrum down even to the nanoscale. It can be used for various reasons, including environmental management, agriculture, and mineralogy. Unlike small data systems, where patterns are simple to identify, in large scale systems, where there is huge data quantity to study, there should be a wide variety of complexity of algorithms to use in reliable anomaly detection. This research bases its methodology on a two-layer mechanism which is simply to conduct very thorough literature review and deep bibliometric analysis to showcase the innovations in the field of anomaly detection of hyperspectral radar. The role of the states of estimation and the parameters, trained and objective, filtered, control rules, and fuzzy logic are some aspects, among the many, that is coming to the fore during the anomaly detection process. This paper aims to carry out a detailed analysis to mainly focus on the technical and methodological advancements that have reshaped the research area. It also shows that priority should be given to this aspect and that anime detection is the most challenging part of hyperspectral. What is more, this process gives numerous valuable signals about potential routes for future research. The results point to the dominant feature of the developed strategy and analysis which was mostly based on the special of multi-dimensional and transdisciplinary thinking.