Name
Abstract | ||
|---|---|---|
The popularity of hyperspectral imaging in remote sensing continues to to be adapted in novel ways to overcome challenging imaging problems. This paper reports on some of the latest research efforts exploring the phenomenology of using hyperspectral imaging as an aid in detecting and tracking human pedestrians. An assessment of the likelihood of distinguishing between pedestrians given observable materials and based on signal-to-noise level is presented. Initial results indicate favorable separability can be achieved with signal-to-noise ratios as low as 13 for certain materials. Additionally, an overview of a real-world urban hyperspectral imaging data collection effort is presented. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6352365 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
geophysical image processing,object detection,object tracking,pedestrian detection,pedestrian tracking,remote sensing,signal-to-noise ratio,urban hyperspectral imaging,Hyperspectral imaging,Pedestrian tracking,Spectral separability,Target detection | Data collection,Computer vision,Object detection,Phenomenology (philosophy),Computer science,Remote sensing,Hyperspectral imaging,Video tracking,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4673-1158-8 | 978-1-4673-1158-8 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jared Herweg | 1 | 0 | 0.34 |
| John P. Kerekes | 2 | 194 | 35.38 |
| Michael T. Eismann | 3 | 326 | 19.71 |