A parametric study of unsupervised anomaly detection performance in maritime imagery using manifold learning techniques

C. Olson, T. Doster
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引用次数: 12

Abstract

We investigate the parameters that govern an unsupervised anomaly detection framework that uses nonlinear techniques to learn a better model of the non-anomalous data. A manifold or kernel-based model is learned from a small, uniformly sampled subset in order to reduce computational burden and under the assumption that anomalous data will have little effect on the learned model because their rarity reduces the likelihood of their inclusion in the subset. The remaining data are then projected into the learned space and their projection errors used as detection statistics. Here, kernel principal component analysis is considered for learning the background model. We consider spectral data from an 8-band multispectral sensor as well as panchromatic infrared images treated by building a data set composed of overlapping image patches. We consider detection performance as a function of patch neighborhood size as well as embedding parameters such as kernel bandwidth and dimension. ROC curves are generated over a range of parameters and compared to RX performance.
基于流形学习技术的海事图像无监督异常检测性能参数化研究
我们研究了控制无监督异常检测框架的参数,该框架使用非线性技术来学习更好的非异常数据模型。流形或基于核的模型是从一个小的,均匀采样的子集中学习的,以减少计算负担,并且假设异常数据对学习模型的影响很小,因为它们的稀罕性降低了它们包含在子集中的可能性。然后将剩余的数据投影到学习空间中,并将其投影误差用作检测统计。在这里,考虑核主成分分析来学习背景模型。我们考虑了来自8波段多光谱传感器的光谱数据,以及通过构建重叠图像块组成的数据集来处理全色红外图像。我们将检测性能视为补丁邻域大小以及嵌入参数(如核带宽和维数)的函数。ROC曲线在一系列参数上生成,并与RX性能进行比较。
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