Linear unmixing of multidate hyperspectral imagery for crop yield estimation

The first contribution of this paper is to evaluate unsupervised linear unmixing approaches for hyperspectral images for crop yield estimation. In this paper we will use a very efficient endmember extraction approach the Vertex Component Analysis (VCA). The second contribution of this paper is to use the hyperspectral images of the same fields taken on two different dates for yield estimation. Even though the images are often taken in good conditions (sunny and calm weather), the light and weather can still have influence on the observation. In addition, calibration errors can randomly occur in an image. The relations between the yield data and different observations will vary. The fusion of different independent observations on the same scene can reduce the influences of this uncertainty and obtain a robust result. In this paper, we propose to combine, for each field, the unmixing results obtained on two different dates in order to improve the accuracy of the estimation.