TY - GEN
T1 - Joint Kurtosis–Skewness-Based Background Smoothing for Local Hyperspectral Anomaly Detection
AU - Wang, Yulei
AU - Zhao, Yiming
AU - Xia, Yun
AU - Chang, Chein I.
AU - Song, Meiping
AU - Yu, Chunyan
N1 - Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - Anomaly detection becomes increasingly important in hyperspectral data exploitation due to the use of high spectral resolution to uncover many unknown substances which cannot be visualized or known a priori. The RX detector is one of the most commonly used anomaly detections algorithms, where both the global and local versions are studied. In the double window model of local RX detection, it is inevitable that there will be abnormal pixels in the outer window where the background information is estimated. These abnormal pixels will cause great interference to the detection result. Aiming at a better estimation of the local background, a joint kurtosis–skewness algorithm is proposed to smooth the background and get better detection results. The skewness and kurtosis are three and four order statistics respectively, which can express the non-Gaussian character of hyperspectral image and highlight the abnormal information of the target. The experimental results show that the proposed detection algorithm is more effective for both synthetic and real hyperspectral images.
AB - Anomaly detection becomes increasingly important in hyperspectral data exploitation due to the use of high spectral resolution to uncover many unknown substances which cannot be visualized or known a priori. The RX detector is one of the most commonly used anomaly detections algorithms, where both the global and local versions are studied. In the double window model of local RX detection, it is inevitable that there will be abnormal pixels in the outer window where the background information is estimated. These abnormal pixels will cause great interference to the detection result. Aiming at a better estimation of the local background, a joint kurtosis–skewness algorithm is proposed to smooth the background and get better detection results. The skewness and kurtosis are three and four order statistics respectively, which can express the non-Gaussian character of hyperspectral image and highlight the abnormal information of the target. The experimental results show that the proposed detection algorithm is more effective for both synthetic and real hyperspectral images.
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U2 - 10.1007/978-981-13-6504-1_71
DO - 10.1007/978-981-13-6504-1_71
M3 - Conference contribution
AN - SCOPUS:85071419774
SN - 9789811365034
T3 - Lecture Notes in Electrical Engineering
SP - 587
EP - 593
BT - Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II
A2 - Liang, Qilian
A2 - Liu, Xin
A2 - Na, Zhenyu
A2 - Wang, Wei
A2 - Mu, Jiasong
A2 - Zhang, Baoju
PB - Springer Verlag
T2 - International Conference on Communications, Signal Processing, and Systems, CSPS 2018
Y2 - 14 July 2018 through 16 July 2018
ER -