Multiview siamese collaborative network for hyperspectral image unmixing

Existing hyperspectral image unmixing methods acquire information from single-view, and therefore can hardly make full use of diverse spectral information, and the learning of space and spectrum is often relatively independent and cannot be combined effectively. In order to solve the problem of insufficient feature representation caused by the single-view spectral information and the lack of close relationship between spatial and spectral learning, we introduce the idea of multiview data construction, which divides the spectral bands into different views for multiview learning. In addition, we proposed a spatial-spectral siamese network. Deep collaborative learning is used to construct an unmixing model by combining multiview representation and the siamese network. Experimental results on the Jasper Ridge and Urban datasets demonstrate the effectiveness of the proposed method.