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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiurui Geng Luyan Ji Yongchao Zhao Fuxiang Wang |
| Copyright Year | 2004 |
| Abstract | This letter proposes a small target detection method for the hyperspectral image based on higher order statistics. This method first calculates the coskewness tensor of the hyperspectral image, followed by the orthogonal decomposition using higher order singular value decomposition. The obtained singular vectors are then used to perform the orthogonal transform to the centralized image. Compared to the popular blind source separation techniques, the presented method keeps clear of nonconvergence. Experiments with a real hyperspectral image show that the interested small target will be presented in the first few bands (even in the first band) very clearly after the transformation. |
| Sponsorship | IEEE Geoscience and Remote Sensing Society |
| Starting Page | 1305 |
| Ending Page | 1308 |
| Page Count | 4 |
| File Size | 873591 |
| File Format | |
| ISSN | 1545598X |
| Volume Number | 10 |
| Issue Number | 6 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Tensile stress Hyperspectral imaging Object detection Vectors Covariance matrix Principal component analysis target detection Coskewness tensor higher order singular value decomposition (HOSVD) hyperspectral data |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology |
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