LAUSR

This Letter presents a method for training convolutional neural networks (CNNs) that detect targets of interest in hyperspectral images. Collecting suitable and abundant training data has been the main obstacle to the successful application of CNNs to hyperspectral target detection. To solve the problem, the authors propose a scheme to generate synthetic training data. Publicly available spectral reflectance library and an easy-to-obtain radiative transfer model are utilised in their scheme. Using the synthetic training data only, a Siamese CNN is trained to learn robust features for the detection task. Experimental results on the real hyperspectral image show the effectiveness of the proposed method.