Author:
Jiang Zongchen,Zhang Jie,Ma Yi,Mao Xingpeng
Abstract
Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.
Funder
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference47 articles.
1. Oil Spill Hyperspectral Remote Sensing Detection Based on DCNN with Multi-Scale Features
2. Research on crude oil film absolute thickness inversion based on self-expanding deep confidence network;Jiang;Ocean. Sci.,2021
3. The Challenges of Interpreting Oil-Water Spatial and Spectral Contrasts for the Estimation of Oil Thick-ness: Examples from Satellite and Airborne Measurements of the Deepwater Horizon Oil Spill;Sun;IEEE Trans. Geosci. Remote Sens.,2019
4. Dual-Polarized TerraSAR-X Data for Oil-Spill Observation
5. Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data
Cited by
23 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献