ABSTRACT

Hyperspectral anomaly detection is a key technique of unsupervised target detection. In the hyperspectral anomaly detection based on spectral dimensional transformation, the feature projection makes it easy to distinguish the ground objects which are not distinguishable in the original feature space. Although the means of spectral dimensional transformation can improve the distinguishable between diverse categories, it cannot highlight the anomalous targets. To be able to highlight anomalous targets while improving the diversity between different ground objects, an unsupervised network model of redundant difference-value network (RDVN) is proposed and applied to hyperspectral anomaly detection. RDVN is composed of multiple single-layer neural networks with the same structure and hyper-parameters. A group of training samples is used as the input of the networks, and the difference between the activation values of any network and benchmark network is used as the error for backpropagation. After the training is completed, the difference-value between the activation values of the two networks is used as a distinguishing feature (DF). Finally, DF is used as the input of the anomaly detector to obtain the detection results. Experimental results demonstrate that the proposed algorithm can achieve higher detection accuracy. DF not only highlights the anomalous target to increase the true positive rate but also increases the discriminability between different categories, thereby reducing the false-positive rate.

摘要

高光谱异常检测是无监督目标检测的关键技术。在基于光谱维变换的高光谱异常检测中，特征投影使区分原始特征空间中无法区分的地面物体变得容易。尽管频谱尺寸变换的方法可以改善不同类别之间的可区分性，但是它不能突出显示异常目标。为了能够在提高不同地面物体之间的多样性的同时突出异常目标，提出了一种冗余差值网络（RDVN）的无监督网络模型，并将其应用于高光谱异常检测中。RDVN由具有相同结构和超参数的多个单层神经网络组成。一组训练样本用作网络的输入，任何网络和基准网络的激活值之间的差用作反向传播的误差。训练完成后，两个网络的激活值之间的差值将用作区分特征（DF）。最后，将DF用作异常检测器的输入以获得检测结果。实验结果表明，该算法可以达到较高的检测精度。DF不仅突出异常目标以增加真实阳性率，而且增加了不同类别之间的可分辨性，从而降低了假阳性率。任何网络和基准网络的激活值之间的差异将用作反向传播的误差。训练完成后，两个网络的激活值之间的差值将用作区分特征（DF）。最后，将DF用作异常检测器的输入以获得检测结果。实验结果表明，该算法可以达到较高的检测精度。DF不仅突出异常目标以增加真实阳性率，而且增加了不同类别之间的可分辨性，从而降低了假阳性率。任何网络和基准网络的激活值之间的差异将用作反向传播的误差。训练完成后，两个网络的激活值之间的差值将用作区分特征（DF）。最后，将DF用作异常检测器的输入以获得检测结果。实验结果表明，该算法可以达到较高的检测精度。DF不仅突出异常目标以增加真实阳性率，而且增加了不同类别之间的可分辨性，从而降低了假阳性率。DF用作异常检测器的输入以获得检测结果。实验结果表明，该算法可以达到较高的检测精度。DF不仅突出异常目标以增加真实阳性率，而且增加了不同类别之间的可分辨性，从而降低了假阳性率。DF用作异常检测器的输入以获得检测结果。实验结果表明，该算法可以达到较高的检测精度。DF不仅突出异常目标以增加真实阳性率，而且增加了不同类别之间的可分辨性，从而降低了假阳性率。