Abstract:
Hyperspectral unmixing is an essential task in hyperspectral imagery applications. Deep learning methods have been taken into hyperspectral unmixing because of its great ...Show MoreMetadata
Abstract:
Hyperspectral unmixing is an essential task in hyperspectral imagery applications. Deep learning methods have been taken into hyperspectral unmixing because of its great feature extraction ability and better performance. However, there are several problems in existing deep learning based spectral unmixing methods. The networks are not deep enough to exploit their feature extraction capabilities in these unsupervised autoencoders based methods, and their effects are not stable. The main reason may be the limited prior information limited the ability of conducting the supervised method. In this manuscript, a semi-supervised deep learning based unmixing method is proposed. Unlike the existing methods, our model uses deeper neural networks without pooling layers, and the endmember spectrum are selected supervised from the original data, which uses nature and nurture cooperatively. The experimental results show that the proposed method achieves better performance and produces more accurate abundance maps, as well as higher quantitative results, compared with the current state-of-the-art deep learning unmixing algorithms.
Date of Conference: 26 September 2020 - 02 October 2020
Date Added to IEEE Xplore: 17 February 2021
ISBN Information: