超光谱成像遥测污染气体的主要目的是对其进行识别分类，进而获得其空间分布信息，确定污染源位置。实际应用中，目标光谱叠加在强背景辐射之上，此外开放路径中的实测光谱还包含大气等干扰物光谱，这些因素制约了对目标光谱的识别分类。在线性模型基础上，利用正交子空间投影方法有效压缩背景及干扰物信息，并基于广义似然比检验原理构建子空间检测器，对所有像元逐个分类识别。以氨气为目标气体进行了野外实验，数据立方体来自扫描成像傅里叶变换红外(FTIR)光谱仪，子空间向量由奇异值分解(SVD)算法得到。结果表明，子空间检测器对所有像元的识别结果优于光谱角度填图(SAM)算法。

Identification and classification is the main purpose of hyperspectral imaging remote sensing pollutant gases, then the spatial distribution of the pollutant gases is obtained, as well as the location of the pollutant source. In practical applications, target spectrum is superimposed on intense background radiation, in addition, the spectra measured in open path comprise atmosphere interferences spectra which restrict identification and classification for target spectra. On the basis of linear model, orthogonal subspace projection method is used to effectively suppress the background and interferences′ information, and the subspace detector, based on gerneralized likelihood ratio test principle, is used to classify all pixels one by one. The field experiment is performed with ammonia as target gas, the data cube comes from scanning imaging Fourier transform infrared spectroscopy (FTIR) spectrometer, and the subspace vectors come from singular value decomposition (SVD). The recognition results for all pixels by subspace detector are superior to spectral angel mapper (SAM) algorithm.