Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector
Puhong Duan, Xudong Kang, Pedram Ghamisi

TL;DR
This paper introduces an unsupervised hyperspectral oil spill detection method using an isolation forest, combining noise filtering, dimensionality reduction, and advanced classification techniques to improve detection accuracy without requiring labeled training data.
Contribution
First unsupervised oil spill detection approach for hyperspectral images utilizing isolation forest and a comprehensive processing pipeline.
Findings
Outperforms existing detection methods in experiments
Effectively removes noisy bands to enhance detection
Achieves high accuracy in complex ocean scenarios
Abstract
Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Hyperspectral remote sensing images provide rich spectral information which is beneficial for the monitoring of oil spills in complex ocean scenarios. However, most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs), which require a huge amount of effort to annotate a certain number of high-quality training sets. In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs. First, considering that the noise level varies among different bands, a noise variance estimation method is exploited to evaluate the noise level of different bands, and the…
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Taxonomy
TopicsOil Spill Detection and Mitigation · Remote-Sensing Image Classification · Marine and coastal ecosystems
