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.

中文翻译：

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使用合成训练数据的连体高光谱目标检测

本文介绍了一种训练卷积神经网络 (CNN) 的方法，该网络可检测高光谱图像中的感兴趣目标。收集合适且丰富的训练数据一直是 CNN 成功应用于高光谱目标检测的主要障碍。为了解决这个问题，作者提出了一种生成合成训练数据的方案。在他们的方案中使用了公开可用的光谱反射库和易于获得的辐射传输模型。仅使用合成训练数据，训练连体 CNN 以学习检测任务的稳健特征。在真实高光谱图像上的实验结果表明了该方法的有效性。