采用差分偏振光谱法在3~5 μm波段对水面溢油污染物进行被动遥感时，所测差分偏振光谱是含有强大气吸收光谱信号与油污染物目标弱光谱信号的混合谱，这给油污染物光谱特征识别带来了困难。另外，受环境因素以及油膜自身张力影响，水面油膜厚度分布以及油膜表面粗糙度在测量过程中发生变化，从而使得连续测量的差分偏振光谱中油污染物光谱信息含量存在不同。利用这一特点，基于固定点迭代的快速主成分分析算法FastPCA设计了水面溢油污染物差分偏振光谱信号预处理算法。实验结果表明，该算法可以有效地将水面油污染物目标光谱特征信息从具有强大气吸收的混合差分偏振光谱信号中提取出来，通过光谱重构得到油污染物光谱特征信号，可用于进一步的定性、定量分析。

When the oil spills on water are passively detected by differential polarization Fourier transform infrared (FTIR) spectroscopy in the band of 3~5 μm, the measured differential polarization spectrum will be a mixed spectrum of the strong atmospheric absorption signal and the minute signal of oil pollutant. It brings complexity to the identification of oil spills on water. In addition, the thickness distribution and the surface coarseness of oil film on water change with the influence of environmental factors and the tension of oil film. This makes the content of effective spectral information, which is contained in the differential polarization spectrum of oil spills on water, change continuously during the process of measurement. Using this characteristic, an algorithm based on a fast fixed-point algorithm for principal component analysis (FastPCA) is proposed for preprocessing the differential polarization spectrum of oil pollutant on water. Experimental results show that this algorithm can separate the spectral information of oil pollutant from the differential polarization spectrum with strong atmospheric absorption signal. The reconstructed spectral characteristic signal of oil pollutant can be used for the further qualitative and quantitative analyses.