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United States Department of Agriculture

Agricultural Research Service

1 - Introduction
2 - Data Sources
3 - Signal-to-Noise
4 - Imaging Theory
5 - Data Quality
6 - Spectral Bands
7 - Conclusions
8 - Resources
9 - Disclaimer
10 - Acknowledgements
Conclusions
Remote Sensing Basics
 

I. Sources of Imagery

II. Signal-to-Noise Ratio (S/N)

III. Imaging Theory: What to Expect

IV. Simple Tools for Checking Image Data Quality

V. Choice of Spectral Bands

VI. Final Thoughts and Suggestions

VII. Resources and References

Disclaimer

Acknowledgements

 

VI. Final Thoughts and Suggestions

Collecting, processing and analyzing airborne remotely sensed data is more difficult than working with data from field portable radiometric instrumentation or satellite data. The platform characteristics (roll, pitch, yaw, airspeed), altitude of data collection (in the atmospheric column versus above or below it), calibration issues (handled by someone else with satellite data or is non-imaging as with field instruments), choices and trade-offs between spatial, spectral, and radiometric information domains, and its intermediate level of technology create challenges for the user.

One suggestion to investigators using airborne imagery that has always proved to be a useful learning experience is to take a ride in a small aircraft or a helicopter at the altitude of most data collection over an area of interest. Take a 35 mm camera and a pair of binoculars. Try and keep the binoculars on a specific feature of the landscape and note the motions of the aircraft. Note ground and sky conditions such as soil moisture, surface winds, atmospheric haze, etc.

Keep the concept of S/N in mind when looking at airborne imagery. You as the user and/or analyst must ultimately decide the answer to one key question: Does the data have an acceptable S/N to warrants its use?


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Last Modified: 8/18/2010
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