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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mianji, F.A. Ye Zhang Yanfeng Gu |
| Copyright Year | 2009 |
| Description | Author affiliation: School of Electronics and Information Technique, Harbin Institute of Technology (Mianji, F.A.; Ye Zhang; Yanfeng Gu) |
| Abstract | A fast and efficient spatial-spectral fusion method for resolution enhancement of hyperspectral imagery is proposed in this paper. A linear mixture model and fully constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional images are processed using a spatial-spectral information correlation model through a learning-based super-resolution mapping technique. To validate the performance of the method, experiments are carried out on real images. The obtained results validate the reliability of the technique. The main advantages of the proposed method include its autonomous nature so that it doesn't need any high resolution secondary source of data, its acceptable performance, and its low computational cost which makes it favorable for realtime target recognition and tracking applications. |
| File Size | 352088 |
| File Format | |
| ISBN | 9781424433940 |
| DOI | 10.1109/IGARSS.2009.5417891 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-12 |
| Publisher Place | South Africa |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image resolution Hyperspectral imaging Spatial resolution Hyperspectral sensors Pixel Libraries Vectors Computational efficiency Area measurement Remote sensing super-resolution mapping fractional image hyperspectral imagery resolution enhancement spectral unmixing |
| Content Type | Text |
| Resource Type | Article |
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