基于EMD的红外遥测光谱信号预处理新方法

利用红外遥测光谱仪远距离快速、准确地探测污染气体并给出定性鉴别结果，必须对遥测光谱进行预处理，消除高频噪声和低频基线的干扰，提取出污染气体的特征光谱信号。针对现有方法的不足，提出采用具有自适应特性的EMD方法，对光谱信号进行无参数分解，提取出高频噪声与低频基线，实现了红外遥测光谱的预处理。经过该方法处理，光谱信号全局评估系数RMS1平均值达到0.141，局部评估系数RMS2平均值达到0.182，综合评估系数RMS*平均值达到0.026，明显优于小波方法。实验结果表明，EMD方法用于红外遥测光谱信号去噪与基线校正，算法简单，运行可靠，可使问题得到有效解决。

New method of preprocessing IR remote sensing spectrum signals based on EMD

To detect and identify pollutant gases in the distance speedily and accurately with IR remote sensing spectrometer,it is necessary to remove high-frequency noise and low-frequency baseline,of which the purpose is to extract feature information. For the deficiencies of the existing methods, EMD was proposed to preprocess IR remote sensing spectrum. EMD is a preprocessing algorithm which works self-adaptively and without parameters. After High-frequency noise and low-frequency baseline was removed, the global assessment factor RMS1, the partial assessment factor RMS2, and the comprehensive assessment factor RMS* reached 0.141, 0.182 and 0.026 respectively. The perfomace of EMD is better than wavelet decomposition method obviously.The result shows that EMD is convenient and reliable for denoising and baseline correction of IR remote sensing spectrum.