基于光谱知识的高光谱图像自动识别方法

针对传统高光谱图像矿物识别方法未能充分利用矿物光谱诊断吸收特征与矿物光谱知识、识别过程人为干预多等问题,提出了一种基于光谱知识的高光谱图像自动识别方法.该方法引入了基于光谱吸收特征与波形特征的光谱知识作为自动识别的标准,利用连续统去除操作增强光谱吸收特征,采取基于光谱主次吸收特征的识别决策策略,建立多级约束准则以提高识别精度及避免误识别,通过利用模拟数据进行算法精度评价并应用航空高光谱成像仪AVIRIS(Airborne Visible/Infrared Imaging Spectrometer)数据进行应用分析与验证.结果表明:当图像信噪比大于200时,识别准确率可以达到80.3%,能够得到良好的识别结果以及较高的精度,并实现了基于高光谱图像的矿物自动识别.

Automatic recognition of hyperspectral image based on spectral knowledge

In order to solve the problems of current methods for mineral recognition from hyperspectral data, such as the requirement for prior information, the failure to make full use of absorption features and the lack of the automation of recognition process, an automatic recognition approach based on the spectral knowledge was proposed. The spectral knowledge library including the spectral information and absorption features was generated as the recognition standard, in which the absorption features were enhanced by removing the continuum of image spectra and library spectra as well. The decision method was proposed based on the major and minor absorption features, and a multi-constraint criterion was established to improve the recognition accuracy and avoid the false recognition. The accuracy evaluation of the proposed approach was performed on the simulated data and the airborne visible/infrared imaging spectrometer (AVIRIS) data as well. Experimental results show that the recognition accuracy reaches 80.3% when the signal-to-noise of image is higher than 200. Fine results with the high accuracy are obtained by the proposed approach, and the mineral automatic recognition from hyperspectral data is achieved simultaneously.