[1]ZENG Xianling,ZHANG Liyan,HU Ronghua.An SVDD algorithm for hyperspectral anomaly detection based on principal component modeling[J].CAAI Transactions on Intelligent Systems,2014,9(3):343-348.[doi:10.3969/j.issn.1673-4785.201309081]
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An SVDD algorithm for hyperspectral anomaly detection based on principal component modeling

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 3 Page number: 343-348 Column: 学术论文—机器学习 Public date: 2014-06-25
Title:
An SVDD algorithm for hyperspectral anomaly detection based on principal component modeling
Author(s):
ZENG Xianling1;  ZHANG Liyan2;  HU Ronghua1
1. Key Laboratory of Resource Environment and GIS of Beijing, Capital Normal University, Beijing 100048, China;
2. Key Laboratory of 3D Information Acquisition and Application of the Ministry of Education, Capital Normal University, Beijing 100048, Chi
Keywords:
principal component modeling; SVDD; local neighborhood clustering; spectral angle cosine; hyperspectral anomaly detection
CLC:
TP751
DOI:
10.3969/j.issn.1673-4785.201309081
Abstract:
An SVDD algorithm based on the principal component modeling is presented for hyperspectral anomaly detection, in order to solve the problem of its low detection rate caused by mixing abnormal points in the process of modeling background. This method extracts the principal components of the background samples by using the hyperspectral image’s spectral signature, and then uses these different components to build different super spheres respectively, forms different single background component SVDD models by these super spheres, finally uses the integrated decision function to judge these SVDD background models to detect any anomalies. The performance of the algorithm is verified by simulated and real data. The results show that the proposed method can obtain a higher detection rate under low false rate than the algorithm based on SVDD, verifying the effectiveness of this proposed method.
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