A new clustering algorithm applicable to multispectral and polarimetric SAR images
The authors applied a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, a 12-dimensional feature vector for each pixel was extracted from the...
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Published in | IEEE transactions on geoscience and remote sensing Vol. 31; no. 3; pp. 634 - 644 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Legacy CDMS
IEEE
01.05.1993
Institute of Electrical and Electronics Engineers |
Subjects | |
Online Access | Get full text |
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Summary: | The authors applied a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, a 12-dimensional feature vector for each pixel was extracted from the scattering matrix. The clustering algorithm partitioned a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and its insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.< > |
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Bibliography: | CDMS Legacy CDMS ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0196-2892 1558-0644 |
DOI: | 10.1109/36.225530 |