This paper presents a glint correction algorithm for high spatial resolution optical remote sensing imagery captured by the ER-2 Airborne Visual Infrared Imaging Spectrometer (AVIRIS). The algorithm employs linear and differential techniques to mitigate sun glint and sky glint effects, encompassing statistical glint reflections resulting from variations in imaging angles within strips and inter-strip variations due to Fresnel reflectance disparities. It aims to diminish Fresnel reflectance diversity on water surfaces and mitigate the distortions induced by glint reflectance during spectral and ocean color inversion. A comparative analysis of spectral and ocean color information in AVIRIS images before and after correction reveals enhanced accuracy following the glint correction. By systematically addressing multiple glint reflections and their ramifications, this method offers a valuable framework for correcting water surface glint in diverse high spatial resolution optical imagery.

Oils spilled into the ocean can form various weathered oils (non-emulsified oil slicks (NEOS), oil emulsions (OE)) which threaten the oceanic and coastal environments and ecosystems. Optical remote sensing has the unique ability to discriminate oil types and quantify oil volumes as their spectral contrasts with oil-free seawater. Here, a deep learning-based model is developed for identification, classification, and quantification of various oil types. Based on the oil-contained datasets collected from 7 satellite sensors from April 2019 to August 2023, the origin, quantity, and spatial distribution of oils spilled from ships and rigs in the China Seas are mapped in detail. We found that oil spill incidents are primarily from ship discharges (85.8%), while platform leaks lead to more oil emulsions (58.6% compared to 13.1% from ships), which illuminates that the drilling oils are the main source of oil spill pollution in China Seas. The spilled oils correlate with major port locations, including offshore Qingdao and Rongcheng, Bohai Bay, the adjacent areas of Beihai, and Hue and Danang in Vietnam. This study provides new insights into the assessment and management of offshore and marine oil spills.

针对现有海面溢油检测技术难以在石油泄漏初期(尚未形成海面大规模油膜覆盖)及时发现油膜的难题,本文在前期基于热红外图像测算海面油膜面积方法研究的基础上,结合油泄漏至海面后油膜的扩散特征,提出了一种基于热红外视频图像监测油膜面积变化以及时识别海面溢油的方法.首先,基于单帧热红外图像处理算法提取海面前景区域(包含油膜区域与相似物干扰区域)并计算各区域所代表的实际物理面积.基于视频图像处理技术跟踪测算前景区域中各连通区域的实际物理面积变化情况,根据各连通区域的面积变化率识别前景区域中是否存在油膜,从而判断海面是否发生溢油.实验结果表明:所提出的方法能有效识别不同黏度的石油泄漏至海面形成的扩散油膜,在水面包含波浪与相似物干扰时也具有良好的识别精度.该方法适用于特定场景下(如码头、船舶等)的溢油事故的鉴别,能为溢油事故的及时发现和预警提供技术支持.

Optical remote sensing is applied in the identification, classification, and quantification of weathered oil spills. The automatic detection of oil spills through optical imaging is yet a challenge because various oils under different sunglint reflections have complex optical image characteristics. Generally, there are two types of weathered oil spills, namely, non-emulsified oil slicks (NEOS) and oil emulsions (OE), which show different image characteristics under various sunglint reflections. The Coastal Zone Imager (CZI) onboard China’s HaiYang-1C/D (HY-1C/D) satellites can provide multispectral images with high spatial resolution and wide coverage for operational monitoring of oil spills. In this study, we applied an adaptive dynamic detector incorporating a built-in oil–water mixture distribution classifier, specifically for different sunglint reflections, to automatically extract oil spills from CZI images. The spatial heterogeneity distribution of various oil spills could be quantified using a novel separability index, and then, the optimal oil–water segmentation proportion and scale could be obtained. Oil spills are discriminated and extracted under different sunglint reflections, with the variable scale detector implemented by tiling sliding windows of classifiers on detection images, from which respective volumes are derived with lower uncertainties. This approach also uses spatial and spectral ancillary information to improve weathered oil extraction confidence. The results show stable variable-scale extraction accuracies of approximately 90% and 80% for NEOS and EO, respectively. Therefore, the spatio–spectral–distribution comprehensive feature provides a new approach for the automatic extraction of oil spills from optical remote sensing images.

It is significant to prevent and supervise marine pollution through estimating the area of oil slick, when oil spill occurs. The existing determination method of the oil slick area has been developed based on visible image, which is influenced by illumination condition changes more easily. Since the thermal infrared image is almost not affected by illumination changes, it is introduced to determinate the area of oil slick. But it is hard to discriminate between oil slicks and look-alikes (the same thermal characteristics as oil film) without background and prior information. So, based on the visible and thermal infrared image fusion, a novel determination method of the oil slick area is proposed in this paper. The oil slick regions of interest (ROI) are extracted with help of the thermal infrared image processing. Then, based on Principal Component Analysis (PCA) fusion between visible and thermal infrared image, the background and priori information are obtained to discriminate the oil slick. The area of the real oil slick is calculated by the pixel area calculation method. The experimental results show that the proposed method can accurately determinate the oil slick area under different illumination, and the mean error is 2.78%. Moreover, compared with other method, the proposed method can achieve better results in both subjective evaluation and objective indicator. It seems to provide novel insights and supports for marine oil spill pollution control.

针对现有油膜检测技术难以准确测算油膜面积且检测精度受天气条件影响大的问题,本文提出了一种基于热红外图像的海面油膜面积测算方法.采用波段为8～14μm的红外热像仪获取海面油膜的热红外图像,对采集的油膜图像进行预处理(灰度化、中值滤波和锐化);基于图像灰度分布特征分割油膜区域(感兴趣区域,ROI),采用形态学操作对ROI进行填充、腐蚀与膨胀,并对ROI进行数学表征;通过像素面积法计算ROI实际物理面积.实验结果表明:在不同的外界天气环境下(如海浪、海风、海雾、不同光照等环境),该方法对不同黏度的石油样品在海面形成的油膜均有良好的检测精度,ROI面积计算平均误差为3.77％.