Name
Title | ||
|---|---|---|
Operational and Performance Considerations of Radiative-Transfer Modeling in Hyperspectral Target Detection |
Abstract | ||
|---|---|---|
Accounting for radiative transfer within the atmosphere is usually necessary to accomplish target detection in airborne/satellite hyperspectral images. In this paper, two methods of accounting for the illumination and atmospheric effects-atmospheric compensation (AC) and forward modeling (FM)-are investigated in their application to target detection. Specifically, several crucial aspects are examined, such as the processing required, the computational complexity, and the flexibility accorded to an imperfect knowledge of acquisition conditions. Real ground-truthed hyperspectral data are employed in order to evaluate the operational applicability of such approaches in a target-detection scenario, as well as their impact on the processing-chain computational complexity. Results indicate that AC is recommended when accurate knowledge of the acquisition conditions is available, and the image has relatively uniform illumination and nonshadowed targets. Conversely, FM is preferred if scene conditions are not well known and when the targets may be subject to varying illumination conditions, including shadowing. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TGRS.2010.2081371 | Geoscience and Remote Sensing, IEEE Transactions |
Keywords | Field | DocType |
atmospheric techniques,computational complexity,geophysical image processing,knowledge acquisition,object detection,radiative transfer,acquisition conditions,airborne hyperspectral images,atmospheric compensation,atmospheric effects,flexibility,hyperspectral target detection,illumination,imperfect knowledge,knowledge acquisition,nonshadowed targets,operational applicability,processing-chain computational complexity,radiative-transfer modeling,real ground-truthed hyperspectral data,satellite hyperspectral images,scene conditions,Atmospheric compensation (AC),forward modeling (FM),hyperspectral imaging,radiative-transfer modeling (RTM),subpixel target detection | Object detection,Computer vision,Remote sensing,Hyperspectral imaging,Ground truth,Pixel,Artificial intelligence,Frequency modulation,Radiative transfer,Mathematics,Knowledge acquisition,Computational complexity theory | Journal |
Volume | Issue | ISSN |
49 | 4 | 0196-2892 |
Citations | PageRank | References |
8 | 0.86 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefania Matteoli | 1 | 152 | 18.05 |
| Emmett J. Ientilucci | 2 | 20 | 4.02 |
| John P. Kerekes | 3 | 194 | 35.38 |