Hybrid Deep Learning and Optimized Feature Selection for Oil Spill Detection in Satellite Images

This study explores the integration of Synthetic Aperture Radar (SAR) imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection. This study proposes a novel hybrid approach for oil spill detection. The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) for improved feature extraction and selection from input SAR imagery. Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images. The BHHO and PCA algorithms were implemented to identify subsets of optimal features from the entire feature dataset extracted by MobileNet. A supplemented hybrid feature set was constructed from the PCA and BHHO-generated features. It was used as input for oil spill detection using the logistic regression supervised machine learning classification algorithm. Several feature set combinations were implemented to test the classification performance of the logistic regression classifier in comparison to that of the proposed hybrid feature set. Results indicate that the highest oil spill detection accuracy of 99.2% has been achieved using the logistic regression classification algorithm, with integrated feature input from subsets identified using the PCA and the BHHO feature selection techniques. The proposed method yielded a statistically significant improvement in the classification performance of the used machine learning model. The significance of our study lies in its unique integration of deep learning with optimized feature selection, unlike other published studies, to enhance oil spill detection accuracy.