石油烃含量定量化的高光谱遥感探测机理研究

Oil is an important energy information and industrial raw materials, but it is also important environmental pollutant. Traditional detection of petroleum hydrocarbon pollutants is time-consuming, laborious, high cost, and can't be used for widespread testing. Petroleum hydrocarbon quantitative detection based on hyperspectral remote sensing can be massive, real-time access to oil hydrocarbon quantitative information. In this project, we collect the the petroleum hydrocarbon content of soil, hydrology, and vegetation and their spectral curve. First of all, to build adaptive discriminant model of soil, hydrology, and vegetation by the high spectral curve, and second, to build high spectral characteristics of petrolem hydrocarbon while elimating the chlorophyll and heavy metal influences on petroleum hydrocarbon quantitative model by high spectral curve. Finally, to study single variable regression, multiple linear regression, principal variables regression and partial least squares regression of hyperspectral quantitative regression model based on the characteristics of the soil, hydrology, and vegetation respectively. In the project,we take preprossing on hyperspctral remote data and usd it for the models builded,then,we compare the calculation results from the ground hyperspectral data and hyperspectral remote sensing data to study the petroleum hydrocarbon quantitation by the hyperspectral remote sensing.

石油是重要的能源资料和工业原料，同时又是重要的环境污染物。常规的石油烃污染物的检测费时、费力、成本高，而且不能做到大面积的检测。基于高光谱遥感的石油烃定量化检测能够大面积、实时的获取石油烃定量化信息。本项目通过获取地表土壤、水文和植被的石油烃含量及光谱曲线，首先，构建土壤、水文和植被的自适应判别模型，其次，研究叶绿素、重金属对石油烃定量化模型的影响，构建针对石油烃的高光谱特征，再次，分别研究基于特征的土壤、水文和植被的单变量回归、多元线性回归、主变量回归和偏最小二乘回归的高光谱定量化回归模型。通过对过境的高光谱遥感数据进行预处理，将数据应用于模型，进行比对分析，研究基于高光谱遥感影像技术的石油烃含量的定量化研究。

基于高光谱遥感的石油烃定量化检测能够进行大面积、实时地获取石油烃信息。本项目首先获取了地表土壤、水纹和植被的石油烃含量以及光谱曲线，研究回归模型。同时，就高光谱遥感影像的数据处理过程，针对纯像元提取、影像增强、数据压缩、地物分类和典型目标探测等环节开展研究，结合高分辨率遥感影像和视频影像的特点，充分利用高光谱空间信息，并将空间信息与光谱信息结合，提出了多种空谱联合的高光谱遥感影像处理算法。