A seed expanding cluster algorithm for deriving upwelling areas on sea surface temperature images
In this paper a novel clustering algorithm is proposed as a version of the Seeded Region Growing (SRG) approach for the automatic recognition of coastal upwelling from Sea Surface Temperature (SST) images. The new algorithm, One Seed Expanding Cluster (SEC), takes advantage of the concept of approximate clustering due to Mirkin (1996, 2013) to derive a homogeneity criterion in the format of a product rather than the conventional difference between a pixel value and the mean of values over the region of interest. It involves a boundary-oriented pixel labeling so that the cluster growing is performed by expanding its boundary iteratively. The starting point is a cluster consisting of just one seed, the pixel with the cold est temperature. The baseline version of the SEC algorithm uses the Otsu’s thresholding method to fine-tune the homogeneity threshold. Unfortunately, this method does not always lead to a satisfactory solution. Therefore, we introduce a self-tuning version of the algorithm in which the homogeneity threshold parameter is abolished and the similarity threshold derived from the approximation criterion also serves as a homogeneity parameter.