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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li-jing Wang Kun Gao Xin-man Cheng Meng Wang Xiang-hu Miu |
| Copyright Year | 2012 |
| Abstract | Anomaly detection is an important fore-processing part in the hyperspectral imagery analysis chain because it can reduce the huge amount of raw data. In the conventional hyperspectral anomaly detection algorithm, the spatial correlation of the background clutters is often neglected. Moreover, the computational costs render the algorithm ineffective without significant data amount reduction. In this paper, an improved anomaly algorithm is proposed, assuming that the background clutter in the hyperspectral imagery is a three-dimensional Gauss-Markov random field. That is, each interested target may be considered with its contiguous regions during detection. The further anomaly detection algorithm is realized by constructing detection operator based on Gauss-Markov estimation parameters in hyperspectral imagery. Simulation results show that the proposed anomaly detection method based on Gauss-Markov model is more effective than the popular detection algorithm in hyperspectral remote sensing imagery. |
| Starting Page | 135 |
| Ending Page | 138 |
| File Size | 188087 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467324069 |
| DOI | 10.1109/ICCIS.2012.21 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-08-17 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Gauss-Markov random field Hyperspectral imagery RX algorithm Markov processes Covariance matrix Clutter Detection algorithms Anomaly detection Hyperspectral imaging |
| Content Type | Text |
| Resource Type | Article |
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