RBF Neural Network for Landmine Detection in Hyperspectral Imaging

In this work, we evaluate different classification algorithms used for multi-target detection in hyperspectral imaging. We took into consideration the scenario of landmine detection in which we compared the performance of each method in various cases. In addition, we introduced the detection of targets using artificial intelligence-based methods in order to obtain better detection performance together with target identification and estimation of its abundance. These algorithms were tested on various types of hyperspectral images where the spectra of the landmines were planted in different proportions in the hyperspectral scenes. The results show the advantage of using our training strategy for radial basis function neural networks (RBFNN) in order to detect, identify and estimate the abundance of the targets in hyperspectral images at the same time. Moreover, the proposed technique requires a comparable computational cost with respect to state of art target detection techniques.