融合机载高光谱图像与星载合成孔径雷达影像的空天遥感多模态海面溢油探测
收稿日期: 2025-07-16
修回日期: 2025-08-18
录用日期: 2025-09-16
网络出版日期: 2025-09-24
基金资助
国家自然科学基金(U2541203);国家自然科学基金(62222116)
Multi-modal marine oil spill detection via airborne hyperspectral image and satellite-borne synthetic aperture radar image fusion in aerospace remote sensing
Received date: 2025-07-16
Revised date: 2025-08-18
Accepted date: 2025-09-16
Online published: 2025-09-24
Supported by
National Natural Science Foundation of China(U2541203)
海面溢油作为一种突发性海洋污染事件，会对海洋及沿岸地区的生态环境、居民安全等造成严重威胁。随着航空航天技术的快速发展，搭载各类传感器的遥感平台成为获取高分辨率、高质量海面溢油影像数据的关键。依托卫星的星载遥感平台能够大范围、实时地发现与监测海面溢油，依托固定翼飞机、无人机等设备的机载遥感平台能够灵活、准确地获取现场遥感数据，为后续相关应急措施的实施提供有效的数据支撑。考虑到多模态遥感影像能够捕获目标的多维度特征、显著提高遥感探测的准确性与鲁棒性，提出了一种基于机载高光谱图像（HSI）数据与星载合成孔径雷达（SAR）数据的多模态海面溢油探测语义分割模型。模型采用了3D卷积模块提取HSI数据的光谱-空间特征作为对SAR特征的补充；设计了多模态互补空洞空间金字塔池化（MCASPP）模块，在多尺度提取SAR图像特征的同时进行多模态信息融合，解决单一模态数据的局限性问题；引入了方向梯度感知和坐标注意力，构建差分定位增强模块（DLEM），增强模型对溢油边缘与细小区域的感知能力。在GMD（Gulf of Mexico Database）数据集上的实验结果表明，所提模型在Kappa系数、平均交并比（mIoU）与F1分数上分别达到99.43%、99.41%与99.47%，优于所选对比模型，表现出其在航空航天遥感信息支撑下较强的溢油特征提取与探测能力。研究成果能够为空天一体化海洋环境的智能监测提供技术支撑。
刘志洋 , 刘权威 , 张玉香 , 董燕妮 . 融合机载高光谱图像与星载合成孔径雷达影像的空天遥感多模态海面溢油探测[J]. 航空学报, 2026 , 47(10) : 632581 -632581 . DOI: 10.7527/S1000-6893.2025.32581
As a sudden Marine pollution event， marine oil spills pose a serious threat to marine and coastal ecosystems as well as human safety. With the rapid development of aerospace technology， remote sensing platforms equipped with various sensors have become the key to obtain high-resolution and high-quality imagery of marine oil spills. Satellite-borne remote sensing platforms enable large-scale and real-time detection and monitoring of oil spills， while airborne platforms based on fixed-wing aircraft， Unmanned Aerial Vehicles （UAVs） and other equipment offer flexible and accurate on-site data acquisition， providing effective data support for the implementation of subsequent relevant emergency response measures. Considering that multi-modal remote sensing imagery can capture multi-dimensional features of targets and significantly improve detection accuracy and robustness， we propose a multi-modal semantic segmentation model for marine oil spill detection based on airborne Hyperspectral Image （HSI） data and satellite-borne Synthetic Aperture Radar （SAR） data. The model utilizes a 3D convolution module to extract spectral-spatial features from HSI data， serving as a supplement to SAR features. a Multi-modal Complement Atrous Spatial Pyramid Pooling （MCASPP） module is designed to extract SAR features at multi-scale and fuse the strengths of both modalities， mitigating the limitations of single-modality. By introducing directional gradient perception and coordinate attention， a Differential Localization Enhancement Module （DLEM） is constructed to enhance the perception ability of the model for the edge and small area of the oil spill. Experimental results on the Gulf of Mexico Database （GMD） demonstrate that the proposed model achieves 99.43%， 99.41% and 99.47% in Kappa coefficient， mIoU （Mean Intersection over Union） and F1 score， respectively， outperforming the selected comparison models. These results show the model’s strong oil spill feature extraction and detection capabilities under the support of aerospace remote sensing information. The research results can provide technical support for the intelligent monitoring of the marine environment with the integration of the integrated space-air observation systems.
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