Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector

In the wake of marine oil exploration and transportation, the accidents of oil spills have occurredfrequently around the world, which leads to the severe pollution of the marine environment and thehuge damage of coastal species [1–6]. On April 20, 2010, the explosion of Deepwater Horizon oildrilling platform led to a severe leakage. Million barrels of oil polluted the Gulf of Mexico with thearea of about 10,000 square kilometers [7, 8]. Due to this accident, the marine ecosystems, such as fishand seabirds, have been seriously destroyed. On June 4, 2011, the Penglai 19-3 oilfield in Bohai Bay,Northeast China, witnessed a serious oil spill incident, which caused the leak of more than 7 thousandtons of oil into the sea [9]. The polluted area was almost 6200 square kilometers. If the oil would notbe timely monitored after the leakage, the oil slick would be washed onto the coast by the sea waves.This situation would pose a huge threat to coastal aquaculture fishery resources and human health.Therefore, it is of great importance to effectively detect oil spills on the sea surface to monitor thedistribution, impact, and volume of oil spills. Hyperspectral data, which provides rich spectral informationfrom the visible to the infrared spectrum, are a good candidate for oil spill detection.

在海洋油气勘探与运输的过程中，全球范围内油污泄漏事故频发，导致海洋环境遭受严重污染，沿海物种遭受巨大损害[1–6]。2010年4月20日，深水地平线钻井平台的爆炸导致严重泄漏。数百万桶原油污染了约1万平方公里的墨西哥湾水域[7, 8]。由于此次事故，海洋生态系统，如鱼类和海鸟，遭受了严重的破坏。2011年6月4日，位于中国东北部渤海湾的蓬莱19-3油田发生了一起严重的油污泄漏事件，导致超过7千吨原油泄漏入海[9]。污染区域面积近6200平方公里。若在泄漏后未能及时监测油污，海浪会将油膜冲刷至海岸，对沿海水产渔业资源及人类健康构成巨大威胁。因此，有效检测海面上油污，以监控油污的分布、影响和体积，显得尤为重要。高光谱数据，能提供从可见光到红外光谱的丰富光谱信息，是油污检测的理想候选技术。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于海洋石油泄漏检测的高光谱遥感基准数据库，包含Matlab格式的高光谱航空影像和对应掩码，数据量达2.2 GB。数据集旨在通过高光谱数据（从可见光到红外光谱）提供丰富的谱段信息，以支持油污分布、影响和体积的监测，适用于气候变化和环境研究领域。