Mathematical morphology for automated analysis of remotely sensed objects in radar images
A 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 i...
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ftnasantrs:oai:casi.ntrs.nasa.gov:19920052591 2023-05-15T18:18:11+02:00 Mathematical morphology for automated analysis of remotely sensed objects in radar images Daida, Jason M. Vesecky, John F. Unclassified, Unlimited, Publicly available JAN 1, 1991 http://ntrs.nasa.gov/search.jsp?R=19920052591 unknown http://ntrs.nasa.gov/search.jsp?R=19920052591 Accession ID: 92A35215 Copyright Other Sources 43 IGARSS '91: Annual International Geoscience and Remote Sensing Symposium; June 3-6, 1991; Espoo; Finland 1991 ftnasantrs 2012-02-15T19:32:50Z A 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 An unpublished version of Thicken, suitable for watersheds transformations of fractal objects, is also presented.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. Other/Unknown Material Sea ice NASA Technical Reports Server (NTRS) |
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NASA Technical Reports Server (NTRS) |
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43 Daida, Jason M. Vesecky, John F. Mathematical morphology for automated analysis of remotely sensed objects in radar images |
topic_facet |
43 |
description |
A 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. |
format |
Other/Unknown Material |
author |
Daida, Jason M. Vesecky, John F. |
author_facet |
Daida, Jason M. Vesecky, John F. |
author_sort |
Daida, Jason M. |
title |
Mathematical morphology for automated analysis of remotely sensed objects in radar images |
title_short |
Mathematical morphology for automated analysis of remotely sensed objects in radar images |
title_full |
Mathematical morphology for automated analysis of remotely sensed objects in radar images |
title_fullStr |
Mathematical morphology for automated analysis of remotely sensed objects in radar images |
title_full_unstemmed |
Mathematical morphology for automated analysis of remotely sensed objects in radar images |
title_sort |
mathematical morphology for automated analysis of remotely sensed objects in radar images |
publishDate |
1991 |
url |
http://ntrs.nasa.gov/search.jsp?R=19920052591 |
op_coverage |
Unclassified, Unlimited, Publicly available |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Other Sources |
op_relation |
http://ntrs.nasa.gov/search.jsp?R=19920052591 Accession ID: 92A35215 |
op_rights |
Copyright |
_version_ |
1766194647360602112 |