Remote sensing image fuzzy multi-center supervised classification method and application
A technology for supervised classification, remote sensing imagery, applied in character and pattern recognition, instruments, computer parts, etc., and can solve problems such as limited accuracy
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[0031] Let the method use the data set as X, and generate particles directly through the unlabeled hierarchical clustering process. Thus, samples with similar spectral properties occur in a given particle. This hierarchical clustering procedure generates a cluster tree, and then uses labeled samples to classify leaves into three types: pure leaves, impure leaves, and unlabeled leaves. Considering the centers of pure leaves as multiple centers of land cover types, the degree of membership of an unlabeled sample within an impure particle nucleus unlabeled particle is determined by the shortest distance between the sample and the multiple centers. Detailed process such as figure 1 Shown:
[0032] 1. Granularity segmentation module
[0033] To model first- and second-order spectral diversity, the entire dataset is decomposed into particles of similar size in spectral space. On the basis of the FCM algorithm, a hierarchical clustering method using weighted Euclidean distance is...
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