Abstract
A method of unsupervised nearest regularized subspace is proposed for anomaly detection in hyperspectral imagery. Based on a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination, for which the weight vector is calculated by distance-weighted Tikhonov regularization. Proposed detector returns the similarity measurement between the testing pixel and its approximation. Experimental results for real hyperspectral data of proposed approach are demonstrated and compared to other traditional detection techniques.
| Original language | English |
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| Title of host publication | 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings |
| Pages | 1055-1058 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | 2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Melbourne, VIC, Australia Duration: 21 Jul 2013 → 26 Jul 2013 |
Publication series
| Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Conference
| Conference | 2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 |
|---|---|
| Country/Territory | Australia |
| City | Melbourne, VIC |
| Period | 21/07/13 → 26/07/13 |
Keywords
- Anomaly Detection
- Hyperspectral Imagery
- Tikhonov regularization
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Li, W., & Du, Q. (2013). Unsupervised nearest regularized subspace for anomaly detection in hyperspectral imagery. In 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings (pp. 1055-1058). Article 6721345 (International Geoscience and Remote Sensing Symposium (IGARSS)). https://doi.org/10.1109/IGARSS.2013.6721345