OIL SPILL DETECTION TECHNIQUE WITH AUTOMATIC UPDATING USING DEEP LEARNING AND OIL SPILL INDEX

Abstract

This paper presents a novel approach for detecting oil spills, employing two distinct methods. The first method involves utilizing an Oil Spill Index (OSI) that analyzes spectral band information, while the second method utilizes a Deep Learning (DL) segmentation model. The outcomes of both methods are then combined to generate training data, and this iterative process is repeated to update the oil spill DL model. The training data used in this approach are obtained from multiple remote sensing platforms including optical and SAR satellite imagery. By employing this proposed method, it is anticipated that high-quality and comprehensive training datasets will be generated, enabling the DL model to perform effectively.