TY - GEN
T1 - Weighted Anomaly Detection Algorithm Based on Local Density
AU - Tian, Yibing
AU - Tang, Linbo
AU - Li, Zhen
AU - Huang, Yun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Anomaly detection for hyperspectral imagery (HSI) has drawn a lot of attention lately. In the past decades, several methods for detecting anomalies in HSI have been developed. The Reed-Xiaoli detector (RXD) is one of the most widely used methods for anomaly detection. However, RXD assumes that background follow the Gaussian distribution, not always conformed to the real case. In order to solve this problem, we improve the RX anomaly detection algorithm by weighting changes of background. First, the concentric double-window model is utilized to obtain the local densities of each background pixels. Then, the local densities are applied to weight background mean and covariance matrix to reduce the influence of abnormal pixels in the background. The experimental results show that our method outperforms other approaches in anomaly detection.
AB - Anomaly detection for hyperspectral imagery (HSI) has drawn a lot of attention lately. In the past decades, several methods for detecting anomalies in HSI have been developed. The Reed-Xiaoli detector (RXD) is one of the most widely used methods for anomaly detection. However, RXD assumes that background follow the Gaussian distribution, not always conformed to the real case. In order to solve this problem, we improve the RX anomaly detection algorithm by weighting changes of background. First, the concentric double-window model is utilized to obtain the local densities of each background pixels. Then, the local densities are applied to weight background mean and covariance matrix to reduce the influence of abnormal pixels in the background. The experimental results show that our method outperforms other approaches in anomaly detection.
KW - anomaly detection
KW - hyperspectral imagery
KW - local RXD
KW - weighted covariance matrix
UR - https://www.scopus.com/pages/publications/85091938840
U2 - 10.1109/ICSIDP47821.2019.9173450
DO - 10.1109/ICSIDP47821.2019.9173450
M3 - Conference contribution
AN - SCOPUS:85091938840
T3 - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
BT - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Y2 - 11 December 2019 through 13 December 2019
ER -