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中南大学学报(英文版)

Journal of Central South University

Vol. 25    No. 1    January 2018

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A background refinement method based on local density for hyperspectral anomaly detection
ZHAO Chun-hui(赵春晖), WANG Xin-peng(王鑫鹏), YAO Xi-feng(姚淅峰), TIAN Ming-hua(田明华)

Information and Communication Engineering College, Harbin Engineering University, Harbin 150001, China

Abstract:For anomaly detection, anomalies existing in the background will affect the detection performance. Accordingly, a background refinement method based on the local density is proposed to remove the anomalies from the background. In this work, the local density is measured by its spectral neighbors through a certain radius which is obtained by calculating the mean median of the distance matrix. Further, a two-step segmentation strategy is designed. The first segmentation step divides the original background into two subsets, a large subset composed by background pixels and a small subset containing both background pixels and anomalies. The second segmentation step employing Otsu method with an aim to obtain a discrimination threshold is conducted on the small subset. Then the pixels whose local densities are lower than the threshold are removed. Finally, to validate the effectiveness of the proposed method, it combines Reed-Xiaoli detector and collaborative-representation-based detector to detect anomalies. Experiments are conducted on two real hyperspectral datasets. Results show that the proposed method achieves better detection performance.

 

Key words: hyperspectral imagery; anomaly detection; background refinement; the local density

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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