基于SAM-SCP组合方法的热红外高光谱影像岩性分类

为了改善传统岩性分类方法中对光谱局部细节特征分析不足的问题,在光谱角分类模型的基础上,有效地融合地物的光谱特征参量,提出了一种新型的光谱角和光谱特征参量（Spectral Angle Mapper-Spectral Characteristic Parameters,SAM-SCP）组合的分类方法。该方法既体现了光谱的形状特征,又能够充分利用光谱的细节特征,解决由于岩性光谱曲线形状相似而识别效果差的问题,提高了分类精度。应用SAM-SCP分别对模拟热红外高光谱数据与真实热红外高光谱数据进行实验,并在SCP的设定过程中,调整主要谷、次要谷所占的权重以获得最佳的分类效果,最终的结果证明：SAM-SCP能对热红外高光谱影像进行有效的岩性分类,分类结果优于传统分类方法。

Lithology Classification of Thermal Infrared Hyperspectral Images based on SAM-SCP

The common rock forming minerals are found to have more obvious spectral characteristics in the thermal infrared spectrum than in the visible and near-infrared spectrum, which makes them easier to be identified and classified in the former case. Consequently, how to effectively identify and extract lithological types from thermal infrared hyperspectral images becomes a hot and difficult issue. The traditional lithologic classification is only based on the spectral shape characteristics and ignores the detailed characteristics of the spectrum. In order to resolve this problem, a new integrated method of Spectral Angle Mapper-Spectral Characteristic Parameters （SAM-SCP） is proposed. It not only utilizes the shape characteristics of the spectrum, but also makes full use of the detailed characteristics of the spectrum, which avoids the poor lithology recognition effect due to the similar shape of the spectrum curve and effectively improves the classification accuracy. The artificial and real thermal infrared hyperspectral data are respectively used for experiments with SAM-SCP. In the process of setting SCP, the weights of primary and secondary valleys were adjusted to obtain the best classification effect. The final results showed that SAM-SCP can effectively classify the thermal infrared hyperspectral images, and can get better classification results than the other traditional classification methods.