谐波分析光谱角制图高光谱影像分类

目的 针对光谱角制图(SAM)分类算法对高光谱像元光谱曲线的局部特征和其辐射强度不敏感,而且易受噪声和维数灾难影响,致使分类效率低和精度较差等缺陷,将谐波分析(HA)技术引入到SAM高光谱影像分类中,提出一种基于谐波分析的光谱角制图(HA-SAM)高光谱影像分类算法.方法 利用HA技术将高光谱影像从光谱维变换到能量谱特征维空间,并提取低次谐波分量及特征系数(谐波余项、相位和振幅),用特征系数组成的向量代替光谱向量,对高光谱影像进行SAM分类.结果 将SAM和HA-SAM同时应用于EO-1卫星的Hyperion高光谱影像分类,通过对比和分析,验证了HA-SAM的优越性,再选择AVIRIS(airborne visible infrared imaging spectrometer)高光谱影像对HA-SAM进行验证,结果表明该算法具有较强的普适性.结论 HA-SAM提高了传统SAM高光谱影像分类的效率和精度,而且适用性较强具有良好的应用前景.

Classification algorithm of hyperspectral imagery by harmonic analysis and spectral angle mapping

Objective The algorithm for spectral angle mapping classification is insensitive to the local characteristics of the spectral curves of hyperspectral image pixels, as well as to its radiation intensity. This algorithm is easily affected by noise and dimension disasters as well, thus lowering classification efficiency and precision. This study presents a model based on harmonic analysis and spectral angle mapping (HA-SAM) to classify hyperspectral imagery.Method HA technology was used to convert hyperspectral imagery from the spectral dimension to the feature dimension of the energy spectrum. The low-order harmonic component and its characteristic coefficients (harmonic remainder, phase, and amplitude) are extracted, and the spectral vector is replaced with the energy spectrum feature vector to classify hyperspectral imagery with SAM.Result SAM and HA-SAM are applied in EO-1 Hyperion hyperspectral image classification. The superiority of HA-SAM is verified through contrasts and analysis. This model also exhibits strong universal applicability on the basis of AVIRIS(airborne visible infrared imaging spectrometer) hyperspectral images.Conclusion HA-SAM not only improves the efficiency and precision of traditional hyperspectral image classification through SAM but also displays strong applicability and application prospects.