Internal solitary waves(ISWs)change the roughness of the sea surface,thus producing dark and bright bands in optical images.However,reasons for changes in imaging characteristics with the solar zenith angle remain unc...Internal solitary waves(ISWs)change the roughness of the sea surface,thus producing dark and bright bands in optical images.However,reasons for changes in imaging characteristics with the solar zenith angle remain unclear.In this paper,the optical imaging pattern of ISWs in sunglint under different zenith angles of the light source is investigated by collecting optical images of ISWs through physical simulation.The experiment involves setting 10 zenith angles of the light source,which are divided into area a the optical images of ISWs in the three areas show dark-bright mode,single bright band,and bright-dark mode,which are consistent with those observed by optical remote sensing.In addition,this study analyzed the percentage of the dark and bright areas of the bands and the change in the relative gray difference and found changes in both areas under different zenith angles of the light source.The MODIS and ASAR images display a similar brightness-darkness distance of the same ISWs.Therefore,the relationship between the brightness-darkness distance and the characteristic half-width of ISWs is determined in accordance with the eKdV theory and the imaging mechanism of ISWs of the SAR image.Overall,the relationship between them in the experiment is almost consistent with the theoretical result.展开更多
The mid-wave infrared band （3-5 #rn） has been widely used for atmospheric soundings. The sunglint impact on the atmospheric parameter retrieval using this band has been neglected because the reflected radiances in t...The mid-wave infrared band （3-5 #rn） has been widely used for atmospheric soundings. The sunglint impact on the atmospheric parameter retrieval using this band has been neglected because the reflected radiances in this band are significantly less than those in the visible band. In this study, an investigation of sunglint impact on the atmospheric soundings was conducted with Atmospheric InfraRed Sounder ob- servation data from 1 July to 7 July 2007 over the Atlantic Ocean. The impact of sunglint can lead to a brightness temperature increase of 1.0 K for the surface sensitive sounding channels near 4.58 #m. This contamination can indirectly cause a positive bias of 4 g kg-1 in the water vapor retrieval near the ocean surface, and it can be corrected by simply excluding those contaminated channels.展开更多
The critical angle is the angle at which the contrast of oil slicks reverse their contrasts against the surrounding oil-free seawater under sunglint.Accurate determination of the critical angle can help estimate surfa...The critical angle is the angle at which the contrast of oil slicks reverse their contrasts against the surrounding oil-free seawater under sunglint.Accurate determination of the critical angle can help estimate surface roughness and refractive index of the oil slicks.Although it’s difficult to determine a certain critical angle,the potential critical angle range help to improve the estimation accuracy.In this study,the angle between the viewing direction and the direction of mirror reflection is used as an indicator for quantifying the critical angle and could be calculated from the solar/viewing geometry from observations of the Moderate Resolution Imaging Spectroradiometer(MODIS).The natural seep oil slicks in the Gulf of Mexico were first delineated using a customized segmentation approach to remove noise and apply a morphological filter.On the basis of the histograms of the brightness values of the delineated oil slicks,the potential range of the critical angle was determined,and then an optimal critical angle between oil slicks and seawater was then determined from statistical and regression analyses in this range.This critical angle corresponds to the best fitting between the modeled and observed surface roughness of seep oil slicks and seawater.展开更多
Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents.Multispectral images have the advantages of high spatial resolution,short revisit period,and wide imaging width,whi...Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents.Multispectral images have the advantages of high spatial resolution,short revisit period,and wide imaging width,which is suitable for large-scale oil spill monitoring.However,in wide remote sensing images,the number of oil spill samples is generally far less than that of seawater samples.Moreover,the sea surface state tends to be heterogeneous over a large area,which makes the identification of oil spills more difficult because of various sea conditions and sunglint.To address this problem,we used the F-Score as a measure of the distance between forecast value and true value,proposed the Class-Balanced F loss function(CBF loss function)that comprehensively considers the precision and recall,and rebalances the loss according to the actual sample numbers of various classes.Using the CBF loss function,we constructed convolution neural networks(CBF-CNN)for oil spill detection.Based on the image acquired by the Coastal Zone Imager(CZI)of the Haiyang-1C(HY-1C)satellite in the Andaman Sea(study area 1),we carried out parameter adjustment experiments.In contrast to experiments of different loss functions,the F1-Score of the detection result of oil emulsions is 0.87,which is 0.03–0.07 higher than cross-entropy,hinge,and focal loss functions,and the F1-Score of the detection result of oil slicks is 0.94,which is 0.01–0.09 higher than those three loss functions.In comparison with the experiment of different methods,the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks,supports vector machine and random forests models,and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods.To verify the applicability of the CBF-CNN model in different observation scenes,we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments,which include two studies areas(study area 2 and study area 3).The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88,which is 0.16–0.24 higher than that of other methods,and the F1-Score of the detection result of oil slicks is 0.96–0.97,which is 0.06–0.23 higher than that of other methods.Based on all the above experiments,we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills,and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images.展开更多
溢油是海洋环境监测的重要目标之一。近年来,光学遥感对海面溢油不同污染类型的识别、分类与估算原理得到阐明,其技术优势获得认可;能为海面溢油监测提供颠覆性的技术支持,提高了溢油的识别精度,实现精细化定量探测。随着中国海洋水色...溢油是海洋环境监测的重要目标之一。近年来,光学遥感对海面溢油不同污染类型的识别、分类与估算原理得到阐明,其技术优势获得认可;能为海面溢油监测提供颠覆性的技术支持,提高了溢油的识别精度,实现精细化定量探测。随着中国海洋水色业务卫星—HY-1C/D(Haiyang-1C/D)的投入应用,其搭载的海岸带成像仪CZI(Coastal Zone Imager)在中国近海溢油监测中体现了较好的效能;但HY-1C/D星CZI载荷开展中国近海溢油业务化监测应用,还需要不断丰富并发展溢油识别提取算法。在HY-1C/D星CZI载荷的高空间分辨率影像中,不同的海面溢油污染类型具有明确的光谱响应特征和形态特征;太阳耀光反射差异,有助于海面溢油的遥感检测,同时也给溢油污染的识别分类与定量估算带来不确定性影响。本研究在CZI载荷数据对海面溢油波段响应基础上,通过溢油海面与背景干扰的耀光反射特征分析,厘清CZI图像中海面耀光干扰的空间分布特点;进一步在优选波段的移动窗口分割及其参数统计基础上,通过对不同分割窗间的耀光形态特征及其相关性判断,实现了CZI图像上海面溢油较高精度的识别与提取。其中,弱耀光条件下油膜提取的平均精度为90.24%、乳化油的平均精度为80.55%;强耀光条件下溢油提取总体效果也较好。面向国产自主海洋水色业务卫星数据,发展溢油光学遥感识别、分类、提取与估算,不仅能促进国产海洋光学卫星的业务化应用,更能为全面掌握中国近海溢油污染状况提供数据参考。展开更多
基金National Natural Science Foundation of China (Nos.61871353 and 42006164)for their support。
文摘Internal solitary waves(ISWs)change the roughness of the sea surface,thus producing dark and bright bands in optical images.However,reasons for changes in imaging characteristics with the solar zenith angle remain unclear.In this paper,the optical imaging pattern of ISWs in sunglint under different zenith angles of the light source is investigated by collecting optical images of ISWs through physical simulation.The experiment involves setting 10 zenith angles of the light source,which are divided into area a the optical images of ISWs in the three areas show dark-bright mode,single bright band,and bright-dark mode,which are consistent with those observed by optical remote sensing.In addition,this study analyzed the percentage of the dark and bright areas of the bands and the change in the relative gray difference and found changes in both areas under different zenith angles of the light source.The MODIS and ASAR images display a similar brightness-darkness distance of the same ISWs.Therefore,the relationship between the brightness-darkness distance and the characteristic half-width of ISWs is determined in accordance with the eKdV theory and the imaging mechanism of ISWs of the SAR image.Overall,the relationship between them in the experiment is almost consistent with the theoretical result.
基金partly supported by the National Oceanic and Atmospheric Administration (NOAA) GOES-R algorithm working group(AWG) and GOES-R Risk Reduction programs (Grant No.NA06NES4400002)
文摘The mid-wave infrared band （3-5 #rn） has been widely used for atmospheric soundings. The sunglint impact on the atmospheric parameter retrieval using this band has been neglected because the reflected radiances in this band are significantly less than those in the visible band. In this study, an investigation of sunglint impact on the atmospheric soundings was conducted with Atmospheric InfraRed Sounder ob- servation data from 1 July to 7 July 2007 over the Atlantic Ocean. The impact of sunglint can lead to a brightness temperature increase of 1.0 K for the surface sensitive sounding channels near 4.58 #m. This contamination can indirectly cause a positive bias of 4 g kg-1 in the water vapor retrieval near the ocean surface, and it can be corrected by simply excluding those contaminated channels.
基金Natural Science Foundation of Jiangsu Province[Grant no.BK20160023] National Natural Science Foundation of China[Grant Nos.41771376,41371014,61675099]the National Key Research and Development Program of China[Grant no.2016YFC1400901].
文摘The critical angle is the angle at which the contrast of oil slicks reverse their contrasts against the surrounding oil-free seawater under sunglint.Accurate determination of the critical angle can help estimate surface roughness and refractive index of the oil slicks.Although it’s difficult to determine a certain critical angle,the potential critical angle range help to improve the estimation accuracy.In this study,the angle between the viewing direction and the direction of mirror reflection is used as an indicator for quantifying the critical angle and could be calculated from the solar/viewing geometry from observations of the Moderate Resolution Imaging Spectroradiometer(MODIS).The natural seep oil slicks in the Gulf of Mexico were first delineated using a customized segmentation approach to remove noise and apply a morphological filter.On the basis of the histograms of the brightness values of the delineated oil slicks,the potential range of the critical angle was determined,and then an optimal critical angle between oil slicks and seawater was then determined from statistical and regression analyses in this range.This critical angle corresponds to the best fitting between the modeled and observed surface roughness of seep oil slicks and seawater.
基金The National Natural Science Foundation of China under contract No.61890964the Joint Funds of the National Natural Science Foundation of China under contract No.U1906217.
文摘Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents.Multispectral images have the advantages of high spatial resolution,short revisit period,and wide imaging width,which is suitable for large-scale oil spill monitoring.However,in wide remote sensing images,the number of oil spill samples is generally far less than that of seawater samples.Moreover,the sea surface state tends to be heterogeneous over a large area,which makes the identification of oil spills more difficult because of various sea conditions and sunglint.To address this problem,we used the F-Score as a measure of the distance between forecast value and true value,proposed the Class-Balanced F loss function(CBF loss function)that comprehensively considers the precision and recall,and rebalances the loss according to the actual sample numbers of various classes.Using the CBF loss function,we constructed convolution neural networks(CBF-CNN)for oil spill detection.Based on the image acquired by the Coastal Zone Imager(CZI)of the Haiyang-1C(HY-1C)satellite in the Andaman Sea(study area 1),we carried out parameter adjustment experiments.In contrast to experiments of different loss functions,the F1-Score of the detection result of oil emulsions is 0.87,which is 0.03–0.07 higher than cross-entropy,hinge,and focal loss functions,and the F1-Score of the detection result of oil slicks is 0.94,which is 0.01–0.09 higher than those three loss functions.In comparison with the experiment of different methods,the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks,supports vector machine and random forests models,and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods.To verify the applicability of the CBF-CNN model in different observation scenes,we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments,which include two studies areas(study area 2 and study area 3).The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88,which is 0.16–0.24 higher than that of other methods,and the F1-Score of the detection result of oil slicks is 0.96–0.97,which is 0.06–0.23 higher than that of other methods.Based on all the above experiments,we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills,and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images.
文摘溢油是海洋环境监测的重要目标之一。近年来,光学遥感对海面溢油不同污染类型的识别、分类与估算原理得到阐明,其技术优势获得认可;能为海面溢油监测提供颠覆性的技术支持,提高了溢油的识别精度,实现精细化定量探测。随着中国海洋水色业务卫星—HY-1C/D(Haiyang-1C/D)的投入应用,其搭载的海岸带成像仪CZI(Coastal Zone Imager)在中国近海溢油监测中体现了较好的效能;但HY-1C/D星CZI载荷开展中国近海溢油业务化监测应用,还需要不断丰富并发展溢油识别提取算法。在HY-1C/D星CZI载荷的高空间分辨率影像中,不同的海面溢油污染类型具有明确的光谱响应特征和形态特征;太阳耀光反射差异,有助于海面溢油的遥感检测,同时也给溢油污染的识别分类与定量估算带来不确定性影响。本研究在CZI载荷数据对海面溢油波段响应基础上,通过溢油海面与背景干扰的耀光反射特征分析,厘清CZI图像中海面耀光干扰的空间分布特点;进一步在优选波段的移动窗口分割及其参数统计基础上,通过对不同分割窗间的耀光形态特征及其相关性判断,实现了CZI图像上海面溢油较高精度的识别与提取。其中,弱耀光条件下油膜提取的平均精度为90.24%、乳化油的平均精度为80.55%;强耀光条件下溢油提取总体效果也较好。面向国产自主海洋水色业务卫星数据,发展溢油光学遥感识别、分类、提取与估算,不仅能促进国产海洋光学卫星的业务化应用,更能为全面掌握中国近海溢油污染状况提供数据参考。
