A Multi-feature Conversion Adaptive Classification of Hyperspectral Image

Spectral similarity measure is an important tool of hyperspectral remote sensing image clas-sification.By setting the threshold to judge the pixel spectrum and the reference spectra is similar ordissimilar.To overcome this problem,this paper proposes a multi-feature conversion adaptive classifi-cation of hyperspectral image,this is done through using spectral similarity measure value as similari-ty patterns.Experimental results show that the proposed methods are,compared with the traditionalSVM method in the overall accuracy of classification,increased by 6.25%and 8.72%,also it impliesthat using simple learnable measures outperforms complex and manually turned techniques used in tra-ditional classification.