In the present study

an improved weighted iterative spectral mixture cycle called WLSMA is proposed based on the spectral unmixing method. First

this method divides the hyperspectral image into many regions and from each subregion endmembers are extracted automatically. Second

the extracted endmembers are clustered to distinguish different endmember classes from the overall type of spectra. We select the most representative endmember spectra from each class within the clustered results and process the optimal spectra using window convolution to establish an endmember spectrum sample library based on In-CoB. Third

an iterative spectral mixture cycle is applied to the image by introducing the compensation weighting matrix into the abundance e stimation. The results of the AVIRIS data set indicate that WLSMA

which combines Fisher’s principle and iterative spectral m ixture theory

increases the separability between similar minerals without a large amount of prior information

and offers greater flexibility and the possibility to improve the understanding and modeling of real-world data.