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Sequential Extraction Thresholding Clustering for Segmentation of Coastal Upwelling on Sea Surface Temperature Images
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Resumo(s)
Coastal upwelling is a process when cold and nutrient-rich water dynamically appears over the surface of the ocean by replacing the warm water. The oceanographers are interested to detect the upwelling regions and corresponding boundaries but to examine the whole process of upwelling they have to work manually on each image, therefore; it increases the workload. The main purpose of this application is to automatically detect the upwelling regions, monitoring environmental changes and the study of fishery resources.
The Seed Expanding Clustering algorithm (SEC) (Nascimento et al., 2015) is a thresholding clustering method for automatic detection of upwelling and delineation of its fronts. The self‐tuning thresholding is derived from the clustering criterion and serves as a boundary regularizer of the growing clusters. The SEC algorithm is shown more than 80% of accuracy rate on the unsupervised automatic recognition of the phenomenon.
The main contribution of this dissertation is threefold. First, the development of a sequential extraction version of the SEC algorithm with a stop condition that takes advantage of the knowledge domain to select seeds and model extracted features. Second, the development of an explosion control procedure to detect the so-called leakage problem. Third, the development of a fusion scheme of unsupervised clustering validation measures.
The experimental comparison of the new iterative version of the SEC algorithm with a new developed iterative version of Adams & Bischof SRG on the unsupervised segmentation of upwelling regions on SST images from different regions of the globe show their competitiveness comparing to other conventional SRG methods.
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Image segmentation automatic thresholding seeded region growing control leakage problem unsupervised validation Sea Surface Temperature (SST) images