Anomaly Detection in Hyperspectral Imagery Based on Spectral Gradient and LLE

Abstract:

The local linear embedding algorithm(LLE) is applied into the anomaly detection algorithm on the basis of the feature analysis of the hyperspectral data. Then, to deal with the problem of declining capacity of identifying the neighborhood caused by the Euclidean distance, an improved LLE algorithm is developed. The improved LLE algorithm selects neighborhood pixels according to the spectral gradient, thus making the anomaly detection more robust to the changes of light and terrain. Experimental results prove the feasibility of using LLE algorithm to solve the anomaly detection problem, and the effectiveness of the algorithm in improving the detection performance.

• Periodical: Applied Mechanics and Materials (Volumes 121-126)
• Pages: 720-724
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.121-126.720
• Online since: October 2011
• Authors: Liang Liang Wang, Zhi Yong Li, Ji Xiang Sun
• Keywords: Anomaly Detection, Hyper-Spectral, Local Linear Embedding, Manifold Learning, Spectral Gradient

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