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Target Detection of Hyperspectral Image Based on Convolutional Neural Networks

机译：基于卷积神经网络的高光谱图像的目标检测

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摘要

Convolutional neural networks (CNN) has been applied in image classification and target detection successfully, however, it is rarely introduced to the field of hyperspectral image (HSI) target detection. Therefore, in this paper, a hyperspectral image (HSI) target detection method based on CNN is proposed. Firstly, the raw HSI data is preprocessed and the spectral information could be obtained. Secondly, to extract the feature information, a CNN is trained and the parameters of the network are adjusted according to a HSI. Finally, the targets will be calibrated according to the extracted features. To estimate the target detection performance of the proposed method, deep belief network (DBN) and SVM methods are compared in the experiment of the real world AVIRIS HSI experiment. Numerical results show that the proposed method has promising prospect in the field of HSI target detection.
机译：卷积神经网络（CNN）已成功应用于图像分类和目标检测，然而，很少被引入超光图像（HSI）目标检测的领域。因此，在本文中，提出了一种基于CNN的高光谱图像（HSI）目标检测方法。首先，可以预处理原始的HSI数据并且可以获得光谱信息。其次，为了提取特征信息，训练CNN，并且根据HSI调整网络的参数。最后，将根据提取的特征进行校准目标。为了估算所提出的方法的目标检测性能，在真实世界Aviris HSI实验的实验中比较了深度信仰网络（DBN）和SVM方法。数值结果表明，该方法在HSI目标检测领域具有希望的前景。

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