18 November 2014 Local anomaly detection algorithm based on sliding windows in spectral space
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Abstract
In this paper, a novel local ways to implement hyperspectral anomaly detector is presented. Usually, the local detectors are implemented in the spatial window of image scene, but the proposed approach is implemented on the windows of spectral space. As a multivariate data, the hyperspectral image datasets can be considered as a low-dimensional manifold embedded in the high-dimensional spectral space. In real environments, nonlinear spectral mixture occurs more frequently. At these situations, whole dataset would be distributed in one or more nonlinear manifolds in high dimensional space, such as a hyper-curve surface or nonlinear hyper-simplex. However, the majority of global and local detectors in hyperspectral image are based on the linear projections. They are established on the assumption that the geometric distribution of datasets is a linear manifold. It is incapable for them to deal with these nonlinear manifold data, even for spatial local data. In this paper, a novel anomaly detection algorithm based on local linear manifold is put forward to handle the nonlinear manifold problems. In the algorithm, the local neighborhood relationships are established in spectral space, and then an anomaly detector based on linear projection is carried out in these local areas. This situation is similar to using sliding windows in the spectral space. The results are compared with classic spatial local algorithm by using real hyperspectral image and demonstrate the effectiveness in improving the weak anomalies detection and decreasing the false alarms.
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Zhiyong Li, Zhiyong Li, Shilin Zhou, Shilin Zhou, Yong Han, Yong Han, Liangliang Wang, Liangliang Wang, "Local anomaly detection algorithm based on sliding windows in spectral space", Proc. SPIE 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 92631G (18 November 2014); doi: 10.1117/12.2068854; https://doi.org/10.1117/12.2068854
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