Automated Labeling of Hyperspectral Images for Oil Spills Classification

Abstract

The constant increase in oil demand caused a huge loss in the form of oil spills during the process of exporting the product, which leads to an increase in pollution, especially in the marine environment. This research assists in providing a solution for this problem through modern technology by detecting oil spills using satellite imagery, more specifically hyperspectral images (HSI). The obtained dataset from the AVIRIS satellite is considered raw data, which leads to the availability of a vast amount of unlabeled data. This was one of the main reasons to propose a method to classify the HSI by automatically labeling the raw data first through unsupervised K-means clustering. The automatically labeled HSI is used to train various classifiers, that are Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (K-NN), to accomplish the optimal accuracy to be comparable with another research accuracy. In addition, the results of the first region of interest (ROI) indicate that the SVM with RBF kernel obtains 99.89% with principle component analysis (PCA) and 99.86% without the PCA, which revealed better accuracy than RF and the K-NN, while in the second ROI the RF obtained 99.9% with PCA and 99.91% without the PCA, better than K-NN and SVM. The region of interests selected lies within the Gulf of Mexico area. This area was selected based on the frequency of usage in previous research in detecting oil spills.