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Abstract
This work addresses the identification of plastic regions in geophysical data, mainly from remote sensing. We develop a new objective method based on mathematical morphology and feature extraction (persistence landscapes) to identify the location of plastic regions in environmental data. An initial data model segmentation is processed by a geodesic transformation, which provides an unfocused and uncorrelated dataset that we enhance by computing a Laplace transform. The transformation is followed by a persistence landscape calculation to create an image descriptor where plastic data is located. The proposed method was evaluated on simulated data and hyperspectral satellite images around the Porto Malta canyon in the Mediterranean Sea. In addition, the proposed procedure permits the subjective evaluation of the data, and some selected cases are shown to illustrate this situation.
