Anomaly Detection with Camouflage Reconnaissance in Spectral Imaging

Abstract Camouflage technology is critical in concealing targets in various environments. Traditional detection methods often rely on human visual observations , which are limited in modern combat scenarios. Spectral imaging technology has emerged as a promising solution to enhance camouflage detection and recognition. This technology captures both spatial and spectral information from targets, allowing for more effective target detection in high-dimensional space. This study focuses on the anomaly detection reconnaissance of camouflaged targets using spectral imagery. Spectral image data of simulated camouflage scenes were collected, enabling the leverage of both spatial and spectral information for target identification. Multispectral sensors were utilized, and the electromagnetic spectrum, including long-range infrared (LWIR) bands, was explored for improved reconnaissance and surveillance. The methodology includes preprocessing steps such as denoising using the Non-Local Global Means algorithm and Canny edge detection. Edge-preserving techniques were also applied to enhance image quality and improve target edge detection. Anomaly detectors, including RX and LRX algorithms, were employed to identify anomalous objects within the images. Moreover, region filling and spectral matching techniques were introduced to refine results further. Region filling addressed noise and filled detected regions effectively, while spectral matching aided in ground truth identification. The novelty of this research lies in its approach to reconnaissance with masking and spectral matching after anomaly detection. While anomaly detection identifies anomalies, it alone cannot recognize the nature of the detected object. In conclusion, this research demonstrates the potential of spectral imaging in anomaly detection and camouflage reconnaissance. Combining spatial and spectral information and employing advanced algorithms can achieve significant enhancements in target detection capabilities in simulated operational environments.