Multispectral imaging systems extend the capabilities of traditional sensor payloads on small tactical reconnaissance drones beyond the visual spectrum, enabling the collection of information that is inaccessible to human perception. This capability could be a significant advantage in camouflage detection, as camouflage typically relies on visual deception but may be ineffective in other spectral ranges. However, this valuable information comes at the cost of an increased processing workload that operating personnel would have to manage. Therefore, deploying multispectral sensors on tactical reconnaissance UAVs could prove more effective when paired with advanced methods to streamline information processing and support operators. In order to investigate this potential, the Institute of Flight Systems at the University of the Bundeswehr Munich conducted four studies in recent years that examine the capabilities and limitations of multispectral imaging systems for camouflage detection in tactical reconnaissance scenarios. In addition to exploring computer-aided detection methods, the studies focus on managing the large numbers of sensor bands in order to improve detection rates and minimize the associated workload. More specifically, sensor performance modelling techniques that identify the most valuable sensor bands under varying environmental conditions are conceptualized, implemented, and evaluated for their effectiveness in enhancing camouflage detection. This thesis organizes these studies into a cumulative body of research by summarizing and linking their major motivations, methodologies, experiments, and key results. Throughout the conducted studies, several novel approaches have been proposed, and a variety of methodologies have been explored. The first study addresses the problem of camouflage detection in general. It shows that spectral anomaly detection is an effective and efficient method for identifying camouflaged targets in multispectral imagery. In order to manage the increased complexity inherent to multispectral sensors, the second study proposes and evaluates a lightweight sensor performance modelling approach. Using this novel method, the performance of each sensor band in terms of camouflage detection can be predicted based on current environmental conditions, allowing the generation of a subset of only the most valuable sensor bands. By considering only this subset in any subsequent application, the complexity associated with the multispectral imaging system can be substantially reduced. The third study builds on the first two studies by investigating a combination of their methodologies, called sensor-managed anomaly detection. In this novel approach, only the most valuable sensor bands are processed with the detection algorithms instead of all available sensor bands. Although this method does not significantly improve detection rates, it offers considerable potential for conserving resources by reducing the amount of data to be processed while maintaining performance, which could be particularly useful given the constrained resources on small tactical drones. The final study expands upon the second study by integrating sensor performance modelling into an optimization approach that generates novel spectral indices specifically designed to expose camouflaged targets. Combined with the spectral anomaly detection methods explored in the first study, these optimized indices provide significant improvements in camouflage detection performance. Furthermore, given the inherently low computational overhead associated with such indices, the proposed approach could prove particularly valuable in tactical reconnaissance scenarios. In summary, the presented approaches demonstrate the high utility of multispectral imaging systems aboard tactical drones for camouflage detection while considering the constraints imposed by the limited computational resources. However, while the results show the effectiveness and indicate promising future applications of the proposed methods, it is important to note that they are based on relatively limited data sources, which might affect their generalizability and reliability. Therefore, further validation in a broader context and with additional data is advised to ensure their validity in different settings. Future research directions could include exploring faster and more efficient spectral anomaly detection methods, exploiting sophisticated and compact hardware for incorporating deep neural networks, and considering hyperspectral imaging systems as sensor technology evolves.
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Multispectral imaging systems extend the capabilities of traditional sensor payloads on small tactical reconnaissance drones beyond the visual spectrum, enabling the collection of information that is inaccessible to human perception. This capability could be a significant advantage in camouflage detection, as camouflage typically relies on visual deception but may be ineffective in other spectral ranges. However, this valuable information comes at the cost of an increased processing workload tha...
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