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- W4285171615 abstract "Hyperspectral unmixing is a popular research topic in hyperspectral processing, aiming at obtaining the ground features contained in the mixed pixels and their proportion. Recently, nonlinear mixing models have received particular attention in hyperspectral decomposition since the linear mixing model cannot suitably apply in the situation that exists in multiple scattering. In this study, we constructed a residual dense autoencoder network (RDAE) for nonlinear hyperspectral unmixing in multiple scattering scenarios. First, an encoder was built based on the residual dense network (RDN) and attention layer. The RDN is employed to characterize multi-scale representations, which are further transformed with the attention layer to estimate the abundance maps. Second, we designed a decoder based on the unfolding of a generalized bilinear model to extract endmembers and estimate their second-order scattering interactions. Comparative experiments between the RDAE and six other state-of-the-art methods under synthetic and real hyperspectral datasets demonstrate that the proposed method achieved a better performance in terms of endmember extraction and abundance estimation." @default.
- W4285171615 created "2022-07-14" @default.
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- W4285171615 date "2022-01-01" @default.
- W4285171615 modified "2023-10-14" @default.
- W4285171615 title "Residual Dense Autoencoder Network for Nonlinear Hyperspectral Unmixing" @default.
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- W4285171615 doi "https://doi.org/10.1109/jstars.2022.3188565" @default.
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