This paper shows three experiments from our HyperGreding'19 campaign that combine multitemporal hyperspectral data to address several essential questions in target detection. The experiments were conducted over Greding, Germany, using a Headwall VNIR/SWIR co-aligned sensor mounted on a drone with a flight altitude of 80 m. Additionally, high-resolution aerial RGB data, GPS measurements, and reference data from a field spectrometer were recorded to support the hyperspectral data pre-processing and the evaluation process for the individual experiments. The focus of the experiments is the detectability of camouflage materials and camouflaged objects. When the goal is to transfer hyperspectral analysis to a practical setting, the analysis must be robust regarding realistic and changing conditions. The first experiment investigates the SAM and the SAMZID approaches for change detection to demonstrate their usefulness for target detection of moving objects within the recorded scene. The goal is to eliminate unwanted changes like shadow areas. The second experiment evaluates the detection of different camouflage net types over two days. This includes camouflage nets in shadows during one flight and brightly illuminated in another due to varying solar elevation angles during the day. We demonstrate the performance of typical hyperspectral target detection and classification approaches for robust detection under these conditions. Finally, the third experiment aims to detect objects and materials behind the cover of camouflage nets by using a camouflage garage. We show that some materials can be detected using an unmixing approach.
Hyperspectral sensors are used to measure the electromagnetic spectrum in hundreds of narrow and contiguous spectral bands. The recorded data exhibits characteristic features of materials and objects. For tasks within the security and defense domain, this valuable information can be gathered remotely using drones, airplanes or satellites. In 2021, we conducted an experiment in Ettlingen, Germany, using a drone-borne hyperspectral sensor to record data of various camouflage setups. The goal was the inference of camouflage detection limits from typical hyperspectral data evaluation approaches for different scenarios. The experimental site is a natural strip of vegetation between two corn fields. Our main experiment was a camouflage garage that covered different target materials and objects. The distance between the targets and the roof of the camouflage garage was modified during the experiment. Together with the target variations, this was done to determine the material dependent detection limits and the transparency of the camouflage garage. Another experiment was carried out using two different types of camouflage nets in various states of occlusion by freshly cut vegetation. This manuscript contains a detailed experiment description, as well as, the first results of the camouflage transparency and occlusion experiment. We show that it is possible to determine the target inside the camouflage garage and that vegetation cover is not suitable additional camouflage for hyperspectral sensors.
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