Journal
ATMOSPHERE
Volume 8, Issue 4, Pages -Publisher
MDPI AG
DOI: 10.3390/atmos8040072
Keywords
prototype-based method; clutter identification; dual-polarization radar
Funding
- China Postdoctoral Science Foundation [2015M580123]
- National Key Basic Research 973 Program of China [2014CB441403, 2013CB430105]
- National Natural Science Foundation of China [41575037, 41605019]
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A prototype-based method is developed to discriminate different types of clutter (ground clutter, sea clutter, and insects) from weather echoes using polarimetric measurements and their textures. This method employs a clustering algorithm to generate data groups from the training dataset, each of which is modeled as a weighted Gaussian distribution called a prototype. Two classification algorithms are proposed based on the prototypes, namely maximum prototype likelihood classifier (MPLC) and Bayesian classifier (BC). In the MPLC, the probability of a data point with respect to each prototype is estimated to retrieve the final class label under the maximum likelihood criterion. The BC models the probability density function as a Gaussian mixture composed by the prototypes. The class label is obtained under the maximum a posterior criterion. The two algorithms are applied to S-band dual-polarization CP-2 weather radar data in Southeast Queensland, Australia. The classification results for the test dataset are compared with the NCAR fuzzy-logic particle identification algorithm. Generally good agreement is found for weather echo and ground clutter; however, the confusion matrix indicates that the techniques tend to differ from each other on the recognition of insects.
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