Name
Abstract | ||
|---|---|---|
Hyperspectral images enable the detection of targets due to the high spectral sampling. The latest generation of sensors also provides an unprecedented spatial resolution which is further exploited in this article to uncover hard to detect anomalies. In particular, we model and estimate the background building upon robust supervised linear unmixing. We benefit from the high resolution of the data to spatially constrain the background. This provides a novel framework for exploiting both the spectral and the energy variations created by the presence of unknown targets to detect them. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/GlobalSIP.2018.8646705 | IEEE Global Conference on Signal and Information Processing |
Keywords | Field | DocType |
Hyperspectral imaging,anomaly detection,linear mixture model | Anomaly detection,Computer science,Remote sensing,Hyperspectral imaging,Sampling (statistics),Image resolution | Conference |
ISSN | Citations | PageRank |
2376-4066 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cecile Chenot | 1 | 0 | 0.34 |
| Mehrdad Yaghoobi | 2 | 73 | 4.92 |
| Mike E. Davies | 3 | 1664 | 120.39 |
| Yoann Altmann | 4 | 229 | 22.58 |