| Popis: |
Polynomial trend surface analysis was applied to three AVHRR images to determine whether regional trends in image radiance can be removed with this procedure. Results suggest that trend surface techniques can be effective in removing region-scale variation in image radiances that are related to uneven illumination, intermittent cloud cover, and variation in the surface temperature field. The dominant effects of illumination in Channel 2 (visible) data, caused by variable sun angle and proximity of the scene to the terminator, can be minimized by removing (subtracting) the first- and second-order trend surfaces from the raw image. These low-order surfaces also remove regional variation in the surface temperature field, which leads to marginal improvement in binary images derived from Channel 4 (infrared) data. Optimum results for both Channel 2 and Channel 4 data are achieved when the third- and fourth-order surfaces are subtracted to remove local temperature and illumination anomalies that occur at smaller spatial scales, primarily in the vicinity of clouds. Application of higher order surfaces fails to improve image quality. There is some indication that the topography of these higher-order surfaces in part maps regional variation in lead density. Use of a best-fit criterion based on a strict variance technique (such as the least-squares method) to define the trend surface limits the effectiveness of the technique in this application. Criteria that allow for data to be weighted based on their distance from the plane about which they cluster are more appropriate to the structure of AVHRR radiance data typical of images that show sea ice. A criterion that incorporates a rule system based on fuzzy logic offers an alternative means of assessing goodness-of-fit that might prove appropriate in this application. |
