Unsupervised linear unmixing of hyperspectral image for crop yield estimation

Multispectral and hyperspectral imagery are often used for estimating crop yield. This paper describes an unsupervised unmixing scheme of hyperspectral images on field in order to estimate the crop yield. From the hyperspectral images, the endmembers and their abundance maps are computed by unsupervised unmixing. The abundance maps are then compared with the crop yield data. The results show the capability for estimating crop yield of the unmixing scheme, thanks to the high correlations between the crop yield data and the abundance maps of the endmembers corresponding to crop, even though the scheme is totally unsupervised.