A DUAL-STREAM CONVOLUTIONAL FEATURE FUSION NETWORK FOR HYPERSPECTRAL UNMIXING

As an important research element in the field of hyperspectral remote sensing, the purpose of hyperspectral unmixing is to decompose the mixed pixels in a hyperspectral image into endmembers and abundance. With the development of deep learning, it also has good prospects for application in the field of hyperspectral unmixing. Existing hyperspectral unmixing networks tend to focus only on the spectral information in the image which neglecting the spatial information. Therefore, a network based on dual-stream convolutional feature fusion for hyperspectral unmixing is proposed to make full use of the correlation between neighboring pixels in the paper, which includes three parts: dual-stream feature extraction, feature fusion and abundance estimation. The unmixing performance of the network is verified on two real datasets and exhibits better performance compared with other state-of-the-art methods.