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A new clustering algorithm applicable to multispectral and polarimetric SAR imagesWe describe an application of 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, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition 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 is 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.
Document ID
19930068214
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Wong, Yiu-Fai
(Lawrence Livermore National Lab. Livermore, CA, United States)
Posner, Edward C.
(California Inst. of Technology Pasadena, United States)
Date Acquired
August 16, 2013
Publication Date
May 1, 1993
Publication Information
Publication: IEEE Transactions on Geoscience and Remote Sensing
Volume: 31
Issue: 3
ISSN: 0196-2892
Subject Category
Earth Resources And Remote Sensing
Accession Number
93A52211
Distribution Limits
Public
Copyright
Other

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