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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Schaum, A. |
| Copyright Year | 2009 |
| Description | Author affiliation: Naval Research Laboratory, Washington, D.C. 20375-5320 (Schaum, A.) |
| Abstract | In autonomous hyperspectral remote sensing systems, the physical causes of false alarms are not all understood. Some arise from vagaries in sensor performance, especially in non-visible wavelengths. Consequently, many false target declarations are characterized simply as outliers, anomalies conforming to no physical or statistical models. Other false alarms arise from clutter spectra too similar to target spectra. To eliminate the recurrence of such difficult errors, deployed systems should allow operator feedback to their signal processing systems. Here we describe how a hyperspectral system using even advanced detection algorithms, based on a elliptically contoured distribution models, can be enhanced by allowing it to learn from its mistakes. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 610543 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424451463 |
| ISSN | 15505219 |
| DOI | 10.1109/AIPR.2009.5466308 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-10-14 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Feedback Hyperspectral imaging Hyperspectral sensors Detection algorithms Signal processing algorithms Equations Covariance matrix Matched filters Laboratories Testing |
| Content Type | Text |
| Resource Type | Article |
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