基于高光谱特征的水上油膜提取与分析研究
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摘要
在海上溢油的评估与清除中,油膜的相对厚度、范围等都是非常重要的参数,直接决定采用的清污策略。遥感手段作为宏观观测方式能够获取上述参数,因此在溢油事故中发挥着越来越重要的作用。
近年来随着传感器技术和数据处理技术的快速发展,航空和星载高光谱遥感研究都取得了丰硕的成果。航空和星载高光谱遥感近乎连续的光谱能够有效地解决传统光学遥感图像上“同物异谱、同谱异物”的问题,从而提供更加准确的溢油信息。但当前对海上油膜高光谱特征研究多孤立地进行光谱特征分析或图像特征提取,未将两者有机地结合起来。在光谱特征分析方面,多以反射率的高低来反映油膜厚度、存在时间等,对基于波谱形态特征的研究较少；在遥感数据应用于实际的潜力评价方面,直接通过光谱反射值在波长处的差异来确定有利波段,忽略了环境噪声等的影响。
针对以上问题,本文在光谱特征分析、遥感数据应用潜力评估和溢油信息提取等方面开展研究。利用小波变换对不同厚度和不同风化时间的膜光谱特征进行研究,探讨了油膜光谱特征与其厚度、风化时间的耦合关系,指出可见光对原油油膜估算的上限约300um,油膜光谱特征变化明显的时间集中在前7d内；通过计算星载Hyperion高光谱数据的环境等效噪声辐射比,利用其波谱响应函数对实测水体、轻柴油、原油等的反射率数据进行归一化处理,对该数据用于海上油膜识别、油种和油膜厚度区分的潜力进行了评估,指出Hyperion能够较好地识别并区分原油油膜的相对厚度,第12波段至第40波段是进行轻柴油油膜识别的有利波段。在多光谱遥感数据溢油信息提取方面提出基于光谱特征的决策树分类方法,根据不同厚度油膜与水体的反射率光谱特征及其在图像上的表征进行有效分类,分类精度达93.7%,完全满足溢油信息提取的精度要求；在高光谱遥感数据溢油信息提取中提出了基于最小噪声分离(MNF)的决策树分类方法,在降低数据维数和噪声的同时增加了不同厚度油膜与海水间的差异性,使分类过程更加快速、分类结果更加准确。
The relative thickness and distribution of the oil film are important parameters, which determine the optimal strategies during assessment and clean-up of the marine oil spills. As a macroscopic observation method, remote sensing plays an increasingly sig-nificant role in the oil spill accident, especially in large-scale oil spills. It has irreplacea-ble advantages over other means.
Over the past several years, the airborne and spaceborne hyperspectral remote-sensing has made great achievements with the development of sensor and data processing technologies. The continuous spectral bands make it possible to distinguish the objects with similar spectrum, so as to provide more accurate information of the oil spill. Current researches focus on the spectral characteristics or imagery feature extrac-tion individually, but do not analyze both of them at the same time. In terms of spectral characteristics analysis, most of the researches showed the changes of the reflectance values with different thickness, weathering phases, etc. Few of them analyzed the mor-phological characteristics of the spectra. The differences of some objects at certain bands were used to evaluate the potential application of hyperspectral data directly, which ignored the impact of environmental noise.
In this paper, the spectral characteristics of oil film on water with varied thick-nesses and phases were investigated and the coupling relationship among them were studied using wavelet transform. The results indicated the maximum thickness that could be discriminated by optical light was300μm, and the changes of spectrum oc-curred in the first7days. Base on the calculation of the environmental noise equivalent radiance of Hyperion data, the reflectance data of water, light diesel and crude oil were filtered by the response function, and the potential of the Hyperion data to identify the oil film and discriminate different oil type and thicknesses was assessed, which showed that the Hyperion data could discriminate crude oil film with varied thicknesses and the bands between the12th and40th were more effective to identify light diesel oil film. A spectral characteristics based decision-tree classification method was proposed to ex-tract oil spill information from multi-spectral imagery. The classification could be processed by analyzing the spectral characteristics and the image features of oil spill, the accuracy of which was93.7%and satisfied the requirement completely. Aiming at the numerous bands of the hyperspectral remote sensing data, the Minimum Noise Frac-tion(MNF) based decision-tree classification was used to extract oil spill information. This method reduced the data dimension and noise effectively, and magnified the dif-ferences between sea water and oil film, which could get a more accurate and rapid re-sult.
近年来随着传感器技术和数据处理技术的快速发展,航空和星载高光谱遥感研究都取得了丰硕的成果。航空和星载高光谱遥感近乎连续的光谱能够有效地解决传统光学遥感图像上“同物异谱、同谱异物”的问题,从而提供更加准确的溢油信息。但当前对海上油膜高光谱特征研究多孤立地进行光谱特征分析或图像特征提取,未将两者有机地结合起来。在光谱特征分析方面,多以反射率的高低来反映油膜厚度、存在时间等,对基于波谱形态特征的研究较少；在遥感数据应用于实际的潜力评价方面,直接通过光谱反射值在波长处的差异来确定有利波段,忽略了环境噪声等的影响。
针对以上问题,本文在光谱特征分析、遥感数据应用潜力评估和溢油信息提取等方面开展研究。利用小波变换对不同厚度和不同风化时间的膜光谱特征进行研究,探讨了油膜光谱特征与其厚度、风化时间的耦合关系,指出可见光对原油油膜估算的上限约300um,油膜光谱特征变化明显的时间集中在前7d内；通过计算星载Hyperion高光谱数据的环境等效噪声辐射比,利用其波谱响应函数对实测水体、轻柴油、原油等的反射率数据进行归一化处理,对该数据用于海上油膜识别、油种和油膜厚度区分的潜力进行了评估,指出Hyperion能够较好地识别并区分原油油膜的相对厚度,第12波段至第40波段是进行轻柴油油膜识别的有利波段。在多光谱遥感数据溢油信息提取方面提出基于光谱特征的决策树分类方法,根据不同厚度油膜与水体的反射率光谱特征及其在图像上的表征进行有效分类,分类精度达93.7%,完全满足溢油信息提取的精度要求；在高光谱遥感数据溢油信息提取中提出了基于最小噪声分离(MNF)的决策树分类方法,在降低数据维数和噪声的同时增加了不同厚度油膜与海水间的差异性,使分类过程更加快速、分类结果更加准确。
The relative thickness and distribution of the oil film are important parameters, which determine the optimal strategies during assessment and clean-up of the marine oil spills. As a macroscopic observation method, remote sensing plays an increasingly sig-nificant role in the oil spill accident, especially in large-scale oil spills. It has irreplacea-ble advantages over other means.
Over the past several years, the airborne and spaceborne hyperspectral remote-sensing has made great achievements with the development of sensor and data processing technologies. The continuous spectral bands make it possible to distinguish the objects with similar spectrum, so as to provide more accurate information of the oil spill. Current researches focus on the spectral characteristics or imagery feature extrac-tion individually, but do not analyze both of them at the same time. In terms of spectral characteristics analysis, most of the researches showed the changes of the reflectance values with different thickness, weathering phases, etc. Few of them analyzed the mor-phological characteristics of the spectra. The differences of some objects at certain bands were used to evaluate the potential application of hyperspectral data directly, which ignored the impact of environmental noise.
In this paper, the spectral characteristics of oil film on water with varied thick-nesses and phases were investigated and the coupling relationship among them were studied using wavelet transform. The results indicated the maximum thickness that could be discriminated by optical light was300μm, and the changes of spectrum oc-curred in the first7days. Base on the calculation of the environmental noise equivalent radiance of Hyperion data, the reflectance data of water, light diesel and crude oil were filtered by the response function, and the potential of the Hyperion data to identify the oil film and discriminate different oil type and thicknesses was assessed, which showed that the Hyperion data could discriminate crude oil film with varied thicknesses and the bands between the12th and40th were more effective to identify light diesel oil film. A spectral characteristics based decision-tree classification method was proposed to ex-tract oil spill information from multi-spectral imagery. The classification could be processed by analyzing the spectral characteristics and the image features of oil spill, the accuracy of which was93.7%and satisfied the requirement completely. Aiming at the numerous bands of the hyperspectral remote sensing data, the Minimum Noise Frac-tion(MNF) based decision-tree classification was used to extract oil spill information. This method reduced the data dimension and noise effectively, and magnified the dif-ferences between sea water and oil film, which could get a more accurate and rapid re-sult.
引文
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