A new method for Anomaly Detection and Target Recognition

Use of unmanned Aerial Vehicles (UAVs) has gained significant importance in the recent years because they are capable of to be used in in civilian and military purposes for reconnaissance, surveillance, disaster relief, among other tasks. In this paper we present new automated anomaly detection and target recognition methodology that can be used on such a UAV. The standard paradigm for anomaly detection and target recognition in hyperspectral imagery (HSI) is to run a detection or recognition algorithm, typically statistical in nature, and visually inspect each high-scoring pixel to decide whether it is an anomaly or background data. A new method of anomaly detection and target recognition in HSI was studied based on a Neural Network (NN). Two multi-layered neural networks are used for anomaly detection and target recognition. The first phase of the model is used to detect anomalies in HSI. The second phase of the model is to use determine whether the anomaly is a predefined target or not. Both networks are trained in accordance with its intended purpose, so increase in performance is provided. This method can be a suitable solution for applications where the unmanned aerial vehicles used.