| Autor: |
Heesung Kwon, Matthew Thielke, Neelam Gupta, Partick Rauss, Patti Gillespie, Dale J. Smith, Dalton Rosario, Nasser M. Nasrabadi |
| Rok vydání: |
2005 |
| Předmět: |
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| DOI: |
10.21236/ada485820 |
| Popis: |
Recently, object detection based on hyperspectral sensors in support of autonomous robotics navigation has been of great interest. Hyperspectral sensors have been widely used for automatic target detection in military applications, mainly because a wealth of spectral information can be obtained through a large number of narrow contiguous spectral channels (often over a hundred). The main purpose of this report is to present detection techniques based on hyperspectral sensing that can effectively identify potentially harmful objects to UGV navigation. The hyperspectral detection techniques used are built on the basic premise that the spectral signatures of objects of interest are in general different than background materials, and the objects of interest can be identified from their surrounding background materials based on spectral analysis of the hyperspectral data. In this report, we first present detailed information on two hyperspectral sensors-a dual band hyperspectral imager and an acousto-optic tunable filter imager that provide hyperspectral data in the infrared and visible bands, respectively. Several anomaly detection and classification techniques newly developed by ARL are then introduced and applied to the hyperspectral data to identify potential obstacles to robotics navigation. Detection performance for each technique is included in this report. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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