In this paper, a new multi-spectral remote sensing image segmentation method based on multi-parameter
semi-supervised spectral clustering (STS3C) is proposed. Two types of instance-level constraints: must-link and
cannot-link are incorporated into spectral cluster to construct semi-supervised spectral clustering in which the self-tuning
parameter is applied to avoid the selection of the scaling parameter. Further, when STS3C is applied to multi-spectral
remote sensing image segmentation, the uniform sampling technique combined with nearest neighbor rule is used to
reduce the computation complexity. Segmentation results show that STS3C outperforms the semi-supervised spectral
clustering with fixed parameter and the well-known clustering methods including k-means and FCM in multi-spectral
remote sensing image segmentation.
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