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.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.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.HA-SAM not only improves the efficiency and precision of traditional hyperspectral image classification through SAM but also displays strong applicability and application prospects.