Name
Abstract | ||
|---|---|---|
The channel 3 data of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA series of weather satellites (NOAA 6-12) are contaminated by instrumentation noise. The signal to noise ratio (S/N) varies considerably from image to image and the between sensor variation in S/N can be large. The characteristics of the channel noise in the image data are examined using Fourier techniques. A Wiener filtering technique is developed to reduce the noise in the channel 3 image data. The noise and signal power spectra for the Wiener filter are estimated from the channel 3 and channel 4 AVHRR data in a manner which makes the filter adaptive to observed variations in the noise power spectra. Thus, the degree of filtering is dependent upon the level of noise in the original data and the filter is adaptive to variations in noise characteristics. Use of the filtered data to improve image segmentation, labeling in cloud screening algorithms for AVHRR data, and multichannel sea surface temperature (MCSST) estimates is demonstrated. Examples also show that the method can be used with success in land applications. The Wiener filtering model is compared with alternate filtering methods and is shown to be superior in all applications tested |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1109/36.295047 | Geoscience and Remote Sensing, IEEE Transactions |
Keywords | Field | DocType |
adaptive filters,atmospheric techniques,filtering and prediction theory,geophysical techniques,geophysics computing,image segmentation,infrared imaging,oceanographic techniques,remote sensing,3.55 to 3.91 mum,AVHRR,Advanced Very High Resolution Radiometer,Fourier technique,IR imaging,IR method,MCSST,NOAA,SST,Wiener filtering,adaptive filter,channel 3,cloud screening algorithm,far infrared remote sensing,image segmentation,minimum distortion,multichannel sea surface temperature,noise reduction,ocean atmosphere,satellite measurement technique,sea surface temperature,signal to noise ratio | Wiener filter,Noise reduction,Noise power,Median filter,Remote sensing,Signal-to-noise ratio,Salt-and-pepper noise,Image noise,Gaussian noise,Mathematics | Journal |
Volume | Issue | ISSN |
32 | 2 | 0196-2892 |
Citations | PageRank | References |
10 | 4.27 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simpson, J.J. | 1 | 10 | 4.27 |
| Stephan R. Yhann | 2 | 28 | 8.11 |