A supervised abundance estimation method for hyperspectral unmixing

Abundance estimation is one of the key steps in hyperspectral unmixing. Usually, abundance estimation is based on linear mixing. However, in real hyperspectral image, this assumption is not physically rigorous enough, because nonlinear mixture may be observed. Nonlinear models present an improvement by considering the microscopic interactions. However, in most cases, a nonlinear unmixing method should assume a specific nonlinear mixture model, and the corresponding abundance estimation process is only applicable for this model. Recently, supervised machine learning, especially deep learning methods, have achieved promising performance in hyperspectral image processing. Supervised learning is able to capture the mapping between input and output data. In this letter, a new supervised abundance estimation method is proposed, which aims to learn the mapping between pixels spectra and the fractional abundances. To overcome the difficulty that no groundtruth is available in real hyperspectral images, we propose a training samples generation strategy based on synthetic data. The major contribution of this work is that the proposed method can handle the abundance estimation problem in a uniform framework without assuming specific linear or nonlinear mixing model. Experiments on both synthetic and real data are conducted to validate the effectiveness of the proposed method.