**4. Discussion**

The subjects tasked with explosive ordnance disposal and the disposal of improvised explosive devices are always exposed to explosive threats and, often, to ambushes. The civilian subjects are generally a single or group of the ground vehicles of a humanitarian demining organization, traveling from camp to the working area and returning, logistics convoys, medical, humanitarian aid, Red Cross, reconstruction convoy, security forces, civilian VIP travelers, or similar. The level of incidents and casualties for civilian vehicles and convoys dominate, when compared to military or security forces. Several survey technologies could be considered as a tool for analysis and decreasing the associated risk. These include hyperspectral, non-linear junction detection (NLJD), LIDAR, longwave infrared, magnetometer, and ground-penetrating radar (GPR) technologies. Several cited references have provided initial insights into these domains, although we focused solely on passive hyperspectral technology. We chose this specifically, due to the lack of data about the considered explosive devices in a realistic, non-laboratory environment. A positive fact is that the hyperspectral imaging sensors used onboard unmanned aerial vehicles (UAVs) can provide pixels smaller than the explosive devices on the ground surface (very high spatial resolution), which is not practical for piloted helicopter platforms. This is the new opportunity provided by hyperspectral sensors, allowing them to serve as an efficient tool for the detection of targets on the ground. In this study, we developed several solutions for modeling and simulating UXOs and landmines, which are suitable for application in civilian security. The conducted research relied on several assumptions; our work has found them to be generally valid.

The possibility of synthetically implanting target spectral images of explosive targets in a hyperspectral image was verified. The true spectral images of UXOs and landmines, measured by hyperspectral imaging sensors, as well as ground- and aerial (UAV and helicopter)-based imagery were fused with the spectra of terrain spectral images. The generalizability of these results is subject to certain limitations:



The goal of the current study was to derive modeling and simulation methods for implanting the spectral data of explosive targets into a hyperspectral terrain scene, not detection methods themselves. Thus, to quantify the success of the modeling and simulation, we considered several hyperspectral classification methods: spectral angle mapping (SAM), cross-correlation, and linear unmixing. SAM was ultimately selected and used in this research. The independent variable of the SAM method was the spectral angle γ, while the dependent variable was the value of the classification raster. The spectral samples (endmembers) representing the targets (UXOs, landmines) were provided by measured true spectral images of full-scale targets or decreased (reduced dimension) targets. The spectral images of the explosive targets were available in the following ranges, and the generalizability of these results is also subject to the following limitations:

The number of endmembers of UXO targets ranged from 19,251 to 45,661; while the number of endmembers of decreased (reduced) UXO targets ranged from 53 to 108. The area of UXO targets ranged from 0.018147 to 0.040482 m2. The number of endmembers of landmines and plastic objects ranged from 1 to 8, while the number of endmembers of decreased (reduced) landmines and plastic objects was 1. The area of landmine targets and plastic objects ranged from 0.00950 to 0.066040 m2.

The landmines and plastic object were excluded from further research in the study, while the available spectral endmember data was limited to one sample per wavelength.


The five targets were analyzed on two terrain spectral data sets; therefore, further research is recommended, including statistically significant cases.
