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Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network

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Journal of Oceanology and Limnology 2024年第3期42卷 728-743页

作者： Ning ZHANG Junfang YANG shanwei liu Yi MA Jie ZHANG College of Oceanography and Space Informatics China University of PetroleumQingdao 266580China First Institute of Oceanography Ministry of Natural ResourcesQingdao 266061China Technology Innovation Center for Ocean Telemetry Ministry of Natural ResourcesQingdao 266061China

The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill *** selection of characteristic bands with strong separability helps to realize the rapid calculation of data on aircraft or in orbit,which will improve the timeliness of oil spill emergency *** the same time,the combination of spectral and spatial features can improve the accuracy of oil spill *** ground-based experiments were designed to collect measured airborne hyperspectral data of crude oil and its emulsions,for which the multiscale superpixel level group clustering framework(MSGCF)was used to select spectral feature bands with strong *** addition,the double-branch dual-attention(DBDA)model was applied to identify crude oil and its *** with the recognition results based on original hyperspectral images,using the feature bands determined by MSGCF improved the recognition accuracy,and greatly shortened the running ***,the characteristic bands for quantifying the volume concentration of water-in-oil emulsions were determined,and a quantitative inversion model was constructed and applied to the AVIRIS image of the deepwater horizon oil spill event in *** study verified the effectiveness of feature bands in identifying oil spill pollution types and quantifying concentration,laying foundation for rapid identification and quantification of marine oil spills and their emulsions on aircraft or in orbit.

关键词： hyperspectral image spectral analysis dimensionality reduction multiscale superpixel level group clustering framework(MSGCF) double-branch dual-attention(DBDA)

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一种考虑地形影响的GNSS-IR冻土冻融形变监测方法

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武汉大学学报（信息科学版） 2024年第1期49卷 77-89页

作者：刘善伟梁承佳万玮张洁马望中国石油大学(华东)海洋与空间信息学院山东青岛266555 北京大学地球与空间科学学院北京100871

多年冻土区地表高程因活动层每年的融化和冻结而发生季节性下沉和隆起,对工程建筑的安全、生态环境的平衡以及全球气候变化等方面都具有重要的影响。利用全球导航卫星系统干涉反射(global navigation satellite systeminterferometricre... 详细信息

多年冻土区地表高程因活动层每年的融化和冻结而发生季节性下沉和隆起,对工程建筑的安全、生态环境的平衡以及全球气候变化等方面都具有重要的影响。利用全球导航卫星系统干涉反射(global navigation satellite systeminterferometricreflectometry,GNSS-IR)遥感对冻土冻融形变进行监测是一种新型技术手段。针对格洛纳斯(GLONASS)、伽利略(Galileo)系统等长重访周期卫星因每日轨迹不重复造成的地形影响,提出了一种考虑地形的冻土冻融形变监测方法,通过引入反射面倾斜角消除地形变化影响,反演得到更接近实际情况的季节性冻融形变。利用位于美国阿拉斯加北部的SG27站点2018年、2019年无雪日GNSS信噪比数据进行了实验,并与现有研究中的反演方法所得结果进行了对比分析,验证了该方法在冻土冻融形变监测中的有效性。实验结果表明,相比于不考虑地形影响的方法,GLONASS和Galileo所得地表高程变化具有更小的离散性以及更小的不确定性,与复合模型拟合的一致性和决定系数R2都有了一定提升,标准差总平均分别减小约28.9%和36.9%,R2总平均分别提高约0.23和0.24,且不可重复轨道每日数据利用率总平均增幅分别约为19.6%和22.8%。研究结果为监测冻土冻融形变提供了有价值的参考,同时拓展了多GNSS系统的GNSS-IR冻土冻融监测应用。

关键词： GNSS-IR 冻土冻融形变信噪比地形

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基于U-NET的双分支海上SAR溢油检测模型

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海洋科学 2024年第7期48卷 1-10页

作者：盛辉曹文俊刘善伟王大伟杨俊芳张杰中国石油大学(华东)海洋与空间信息学院山东青岛266580

为提高海上溢油SAR(Synthetic Aperture Radar)检测的准确率,本文提出一种基于U-NET和注意力门的海上溢油SAR检测模型(AW-net),该模型将U-NET中传统的单输入编码器替换为双分支编码器,分别输入纹理特征和SAR灰度特征,并进一步采用注意... 详细信息

为提高海上溢油SAR(Synthetic Aperture Radar)检测的准确率,本文提出一种基于U-NET和注意力门的海上溢油SAR检测模型(AW-net),该模型将U-NET中传统的单输入编码器替换为双分支编码器,分别输入纹理特征和SAR灰度特征,并进一步采用注意力门融合纹理信息和灰度信息。实验利用1景海丝一号(HISEA-1)SAR数据构建样本训练集进行AW-net模型训练,分别应用1景HISEA-1 SAR数据和1景Radarsat-2SAR数据开展模型测试,溢油检测准确率均优于U-NET、AttentionU-NET和FCN等语义分割模型,说明该模型具有较强的强鲁棒性和应用潜力。

关键词：溢油检测 SAR U-NET 注意力门双分支编码器

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基于样本增强和自动参数优化的高光谱目标检测方法

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遥测遥控 2024年第4期45卷 31-44页

作者：刘浩许明明沈彪群刘善伟盛辉中国石油大学(华东)海洋与空间信息学院青岛266580 山东鲁邦地理信息工程有限公司济南250102

基于深度学习的高光谱目标检测面临着样本质量不足、网络结构复杂、参数调整费力等问题,本文提出了一种具有数据增强和自动超参数优化的深度学习方法。为了解决样本质量不足的问题,本文引入了一种样本扩增策略。该策略利用端元提取和聚... 详细信息

基于深度学习的高光谱目标检测面临着样本质量不足、网络结构复杂、参数调整费力等问题,本文提出了一种具有数据增强和自动超参数优化的深度学习方法。为了解决样本质量不足的问题,本文引入了一种样本扩增策略。该策略利用端元提取和聚类技术直接从高光谱图像中获取大量背景像素,通过使用相减像素配对方法将这些像素与少量已知目标像素配对,获得大量标记的纯样本对,从而实现数据增强。此外,与大多数复杂的深度网络不同,本文设计了一个由12个卷积层组成的轻量级卷积神经网络(CNN)。该网络专门设计用于高效快速地学习输入样本对与其对应标签之间的映射。结合粒子群优化算法,该网络具有超参数自动优化的能力,克服了参数调整费力的缺点,这使得网络能够根据来自不同高光谱图像的样本自动调整超参数,从而产生最优结果。对于测试像素,训练网络的输入是中心像素与其相邻像素之间的光谱差。当一个测试像素属于目标时,输出分数接近1,反之则接近0。在五个高光谱数据集上的实验结果表明:本文提出的方法明显优于现有的技术。

关键词：高光谱目标检测样本增强卷积神经网络

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A new decomposition model of sea level variability for the sea level anomaly time series prediction

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海洋湖沼学报(英文) 2023年第5期41卷 1629-1642页

作者： Qinting SUN Jianhua WAN shanwei liu Jinghui JIANG Yasir MUHAMMAD College of Oceanography and Space Informatics China University of Petroleum(East China)Qingdao 266580China Technology Innovation Center for Maritime Silk Road Marine Resources and Environment Networked Observation Ministry of Natural ResourcesQingdao 266580China

Rising sea level is of great significance to coastal societies;predicting sea level extent in coastal regions is *** carrying out predictions,the subsequences obtained using decomposition methods may exhibit a certain regularity and therefore can provide multidimensional information that can be used to improve prediction *** decomposition methods such as seasonal and trend decomposition using Loess(STL)focus mostly on the fluctuating trend of time series and ignore its impact on *** in the signal decomposition domain,such as variational mode decomposition(VMD),have no physical *** response to the above problems,a new decomposition method for sea level anomaly time series prediction(DMSLAP)is *** this method,the trend term in a time series can be isolated and the effects of abnormal sea level change behaviors can be *** decompose multiperiod characteristics using this method while maintaining the smoothness of the analyzed *** altimetry data from 1993 to 2020 are used in experiments conducted in the study *** results are then compared with predictions obtained using existing decomposition methods such as the STL and VMD methods and time varying filtering based on empirical mode decomposition(TVF-EMD).The performance of DMSLAP combined with a prediction method resulted in optimal sea level anomaly(SLA)predictions,with a minimum root mean square error(RMSE)of 1.40 cm and a maximum determination coefficient(R^(2))of 0.93 during *** DMSLAP method was more accurate when predicting 1-year data and 3-year *** TVF-EMD and DMSLAP methods had comparable accuracies,and the periodic term decomposed by the DMSLAP method was more in line with the actual law than that derived using the TVF-EMD ***,DMSLAP can decompose SLA time series better than existing methods and is an effective tool for obtaining short-term SLA prediction.

关键词： time series decomposition satellite altimetry China Sea and its vicinity sea level change

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基于高光谱和LiDAR的黄河口湿地植被分类方法

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遥测遥控 2024年第3期45卷 102-113页

作者：许明明刘航窦庆文刘善伟盛辉中国石油大学(华东)测绘系青岛266580 山东省国土测绘院济南250102

利用无人机(Unmanned Aerial Vehicle,UAV)高光谱影像(Hyper-spectral Imaging,HSI)和激光雷达(Light Detection and Ranging,LiDAR)数据开展黄河口湿地植被分类方法研究。由于高空间分辨率HSI光谱变异性强,以及LiDAR点云密度不均匀,分类结果呈现出“椒盐”现象。为了解决这些问题,本文提出了一种结合空谱特征融合和通道注意力机制的双分支卷积神经网络(SSF-C-DBCNN)。光谱注意力机制通过为每个波段分配不同的权重来减少光谱变异性的影响。空间注意力机制侧重于学习和强调特征表达能力强的密集点云区域空间信息,从而减轻LiDAR点云密度不均匀对结果的影响。最后,在双分支融合特征后引入通道注意力机制来提取更深层次的特征。利用UAV采集的HSI和LiDAR数据进行实验验证,结果表明,本文提出方法的性能优于随机森林和五种深度学习方法,分类结果更为贴合实际土地覆盖,有效地抑制了“椒盐”现象。

关键词：分类无人机高光谱激光雷达深度学习注意力机制

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基于三维卷积残差网络的无人机高光谱岩性分类

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遥测遥控 2024年第3期45卷 114-122页

作者：盛辉牟泓宇刘善伟崔建勇中国石油大学(华东)海洋与空间信息学院青岛266033

岩性识别和分类是地质学、资源勘查等不可或缺的环节,高光谱遥感的兴起为岩性识别提供新的思路。利用机器学习挖掘岩石高光谱图像中的信息从而准确识别岩性,这具有重要的应用价值。目前用机器学习的方法实现岩石的高光谱影像分类研究中... 详细信息

岩性识别和分类是地质学、资源勘查等不可或缺的环节,高光谱遥感的兴起为岩性识别提供新的思路。利用机器学习挖掘岩石高光谱图像中的信息从而准确识别岩性,这具有重要的应用价值。目前用机器学习的方法实现岩石的高光谱影像分类研究中,缺少对空间和光谱信息的充分利用,因此本文使用了一种加入注意力机制的三维卷积残差网络结构,能够有效提取岩石高光谱图像的空间、光谱特征以及空谱联合特征。本实验利用无人机搭载高光谱传感器采集了10种不同类型的岩石样本影像,应用该算法对岩石高光谱图像进行分类。实验结果表明:该算法与传统机器学习算法SVM、RF和深度学习算法ResNet、3D CNN和SSRN相比具有更高的精度。

关键词：高光谱图像无人机岩石分类深度学习

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Performance Improvement of Single Screw Compressor by Meshing Clearance Adjustment Used in Refrigeration System

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Journal of Thermal Science 2021年第1期30卷 149-164页

作者： LU Yuanwei liu shanwei WU Yuting LEI Biao ZHI Ruiping WEN Qiangyu MA Chongfang Key Laboratory of Enhanced Heat Transfer and Energy Conservation Ministry of EducationCollege of Environmental and Energy EngineeringBeijing University of TechnologyBeijing 100124China Key Laboratory of Heat Transfer and Energy Conversion Beijing MunicipalityCollege of Environmental and Energy EngineeringBeijing University of TechnologyBeijing 100124China

The single screw compressor(SSC)is widely used in air compression and refrigeration systems due to its many *** meshing clearance between the screw groove and gate rotor teeth flank has a significant influence on the compressor *** this paper,mathematical calculation models describing the internal working process of the SSC are established in order to evaluate the thermal dynamic characteristics of the compressor under varying meshing clearance *** refrigerating capacity,volume efficiency and adiabatic efficiency of the SSC are calculated and *** prototypes,with different meshing clearance heights,were manufactured to study the internal influence *** theoretical model was verified using experimental data and the calculation results strongly agreed with the experimental *** demonstrate that comparisons of volume efficiency and adiabatic efficiency between the measured and calculated results exhibited deviations of 3.64%-7.98%and 5.92%-9.4%,*** on the models,analysis under varying meshing clearance heights and working conditions was *** into account working performance,actual manufacturing conditions and manufacturing cost limitations,a meshing clearance height range from 0.01 mm to 0.08 mm is *** study can provide important theoretical data and experimental support for the design,manufacturing and optimization of single screw compressors.

关键词： single screw compressor leakage performance improvement meshing clearance height refrigeration

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A fast,edge-preserving,distance-regularized model with bilateral filtering for oil spill segmentation of SAR images

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Journal of Oceanology and Limnology 2021年第4期39卷 1198-1210页

作者： Wandi WANG Hui SHENG Yanlong CHEN shanwei liu Jijun MAO Zhe ZENG Jianhua WAN China University of Petroleum Qingdao 266580China National Marine Environmental Monitoring Centre Dalian 116023China Surveying and Mapping Institute of Shandong Province Jinan 250102China

Marine oil spills are among the most significant sources of marine *** aperture radar(SAR)has been used to improve oil spill observations because of its advantages in oil spill detection and ***,speckle noise,weak boundaries,and intensity inhomogeneity often exist in the oil spill regions of SAR imagery,which will seriously aff ect the accurate identification of oil *** enhance marine oil spill segmentation of SAR images,a fast,edge-preserving framework based on the distance-regularized level set evolution(DRLSE)model was ***,a bilateral filter penalty term is designed and incorporated into the DRLSE energy function(BF-DRLSE)to preserve the edges of oil spills,and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time *** sets of RadarSat-2 SAR data were used to test the proposed *** experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set *** with other methods,the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy(97.83%).In addition,using an appropriate initial box boundary for the DRLSE method accelerated the global search process,improved the accuracy of oil spill segmentation,and reduced computational ***,the results suggest that the proposed framework is eff ective and applicable for marine oil spill segmentation.

关键词： level sets bilateral filter marine oil spill segmentation synthetic aperture radar(SAR)imagery

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Ulva prolifera subpixel mapping with multiple-feature decision fusion

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Journal of Oceanology and Limnology 2023年第3期41卷 865-880页

作者： Jianhua WAN Xianci WAN Lie SUN Mingming XU Hui SHENG shanwei liu Bin ZOU Qimao WANG College of Oceanography and Space Informatics China University of Petroleum(East China)Qingdao 266580China Qingdao Ecological and Environment Monitoring Center of Shandong Province Qingdao 266003China National Satellite Ocean Application Service Beijing 100081China Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural Resources(MNR)Beijing 100081China

The unavoidable nature of Ulva prolifera mixed pixel in low-resolution remote sensing images would result in rough boundary of *** patches,omission of tiny patches,and overestimation of coverage *** decomposition of *** mixed pixel addresses the issue of coverage area overestimation,and the remaining problems can be alleviated by subpixel mapping(SPM).Due to the drift and dissipation of ***,a suitable SPM method is the single image-based unsupervised ***,the method has difficulties in detail reconstruction,insufficient learning of spectral information,and SPM error introduced by abundance ***,we proposed a multiple-feature decision fusion SPM(MFDFSPM)*** involves three branches to obtain the spatial,abundance,and spectral features of *** while considers multi-feature information using the fusion *** on the Geostationary Ocean Color Imager images in the Yellow Sea of China indicate that the MFDFSPM overperforms several typical *** SPM methods in higher accuracy and stronger robustness in both SPM and abundance calculation,which produced subpixel map with more detailed spatial information and less noise.

关键词： Ulva prolifera subpixel mapping multiple-feature decision fusion abundance geostationary ocean color imager(GOCI)

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