Feature enhancement in AVHRR imagery via probabilistic relaxation labeling methods ...
A set of algorithms is described which results in the detection, enhancement, extraction,-^, and identification of features in NOAA-9 AVHRR imagery. Sea ice leads (cracks) in ice images from the Beaufort Sea / Amundsen Gulf area are modelled as "lines" in the image-processing sense. Therma...
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| Format: | Text |
| Language: | English |
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University of British Columbia
2010
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| Online Access: | https://dx.doi.org/10.14288/1.0053152 https://doi.library.ubc.ca/10.14288/1.0053152 |
| Summary: | A set of algorithms is described which results in the detection, enhancement, extraction,-^, and identification of features in NOAA-9 AVHRR imagery. Sea ice leads (cracks) in ice images from the Beaufort Sea / Amundsen Gulf area are modelled as "lines" in the image-processing sense. Thermal gradients on the ocean surface are modelled as edges. Emphasis is given to enhancing the output of local line / edge detectors in order to provide improved input for line / edge tracking algorithms. As a result the identification scheme operates on a segmented version of the image rather than on a pixel by pixel basis, thereby providing a less noisy classification. Line / edge enhancement is achieved using the non-linear probabilistic relaxation model of Rosenfeld et al (1976). Results from the relaxation of line detector output suggests that an expanded label (i.e. line orientation) set is preferable to the smaller set suggested by previous studies. Also, a modified form of the original non-linear model (suggested by ... |
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