Pattern recognition of clouds and ice in polar regions
The study is based on AVHRR imagery and results from Landsat high-spatial-resolution scenes. Among the textual features investigated are the gray level difference vector (GLDV), and sum and difference histogram (SADH) approaches as well as gray level run length, spatial-coherence, and spectral-histo...
| Main Authors: | , , , , |
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| Format: | Other/Unknown Material |
| Language: | unknown |
| Published: |
1990
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| Subjects: | |
| Online Access: | http://ntrs.nasa.gov/search.jsp?R=19910051991 |
| Summary: | The study is based on AVHRR imagery and results from Landsat high-spatial-resolution scenes. Among the textual features investigated are the gray level difference vector (GLDV), and sum and difference histogram (SADH) approaches as well as gray level run length, spatial-coherence, and spectral-histogram measures. The traditional stepwise discriminant analysis and neural-network analysis are used for the identification of 20 Arctic surface and cloud classes. A principal-component analysis and hybrid architecture employing a modularized competitive learning layer are utilized. It is pointed out that the cloud-classification accuracy comparable to that of back-propagation could be achieved with a training time two orders of magnitude faster. |
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