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 coldest temperature. The baseline version of the SEC algorithm uses 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 is locally derived from the approximation criterion over a window around the pixel under consideration. The window serves as a boundary regularizer. These two unsupervised versions of the algorithm have been applied to a set of 28 SST images of the western coast of mainland Portugal, and compared against a supervised version fine-tuned by maximizing the F-measure with respect to manually labeled ground-truth maps. The areas built by the unsupervised versions of the SEC algorithm are significantly coincident over the ground-truth regions in the cases at which the upwelling areas consist of a single continuous fragment of the SST map. (C) 2015 Elsevier Ltd. All rights reserved.