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21 February 2014 Applied low dimension linear manifold in hyperspectral imagery anomaly detection
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In this paper, a new approach of anomaly detection based on low dimensional manifold will be elaborated. Hyperspectral image data set is considered as a low-dimensional manifold embedded in the high-dimensional spectral space, and this manifold has special geometrical structure, such as Hyper-plane. Usually, the main body of this manifold is constituted by a large area of background spectrum while the anomalistic objects are outside of the manifold. Through the analysis of the geometrical characteristics and the calculation of the appropriate projection direction, anomalistic objects can be separated from background effectively, so as to achieve the purpose of anomaly detection. Experimental results obtained from both the ground and airborne spectrometer data prove effectiveness of the algorithm in improving the detection performance. Since there are no available prior target spectrums to provide proper projected direction, the weak anomalies which have subtle differences from the background on the spectrum will be undetected.
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Zhiyong Li, Liangliang Wang, and Siyuan Zheng "Applied low dimension linear manifold in hyperspectral imagery anomaly detection", Proc. SPIE 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013, 91421P (21 February 2014);

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