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Mathematical morphology for automated analysis of remotely sensed objects in radar imagesA symbiosis of pyramidal segmentation and morphological transmission is described. The pyramidal segmentation portion of the symbiosis has resulted in low (2.6 percent) misclassification error rate for a one-look simulation. Other simulations indicate lower error rates (1.8 percent for a four-look image). The morphological transformation portion has resulted in meaningful partitions with a minimal loss of fractal boundary information. An unpublished version of Thicken, suitable for watersheds transformations of fractal objects, is also presented. It is demonstrated that the proposed symbiosis works with SAR (synthetic aperture radar) images: in this case, a four-look Seasat image of sea ice. It is concluded that the symbiotic forms of both segmentation and morphological transformation seem well suited for unsupervised geophysical analysis.
Document ID
19920052591
Document Type
Conference Paper
Authors
Daida, Jason M. (NASA Headquarters Washington, DC United States)
Vesecky, John F. (Stanford University CA, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1991
Subject Category
EARTH RESOURCES AND REMOTE SENSING
Meeting Information
IGARSS ''91: Annual International Geoscience and Remote Sensing Symposium(Espoo)
Distribution Limits
Public
Copyright
Other