基于改进光谱特征拟合算法的高光谱数据矿物信息提取

摘要

Spectral feature fitting (SFF) algorithm has been frequently used since 1990s. A modified spectral feature fitting method is introduced here, which can solve some drawback of the general algorithm. The method mentioned here combines SFF with user-defined constraints in spectral absorption feature to extract more accurate target information from hyperspectral image.Two experiments are presented herein, in which three algorithms are used to obtain mineral information from hyperspectral data with different space resolution and SNR. Muscovite, calcite and chloritc etc. are extracted by general SFF, modified SFF and spectral angle mapping (SAM) respectively, and the result indicates that modified SFF algorithm is more effective in differentiating subtle spectral feature and obtaining accurate mineral information. The experiments also demonstrate that the algorithm mentioned here is validated in mineral information extraction.%根据光谱特征拟合算法在实际应用中存在的问题,介绍一种改进光谱特征拟合算法,该算法综合常规的特征拟合处理和地物光谱吸收特征参量约束为一体,能更细致地进行高光谱数据地物信息提取.实验基于不同空间分辨率和信噪比的高光谱数据,编程实现改进光谱特征拟合算法对实验区的白云母、方解石、绿泥石等蚀变矿物信息提取,与常规光谱特征拟合和光谱角制图处理结果的比较分析发现改进算法在矿物混淆区分、信息提取精细度上均得到提高,有较强的实用性.