###### Abstract  
The purpose of hyperspectral unmixing (HU) is to obtain the spectral features of materials (endmembers) and their proportion (abundance) in a hyperspectral image (HSI). Due to the existence of spectral variabilities (SVs), it is difficult to obtain accurate spectral features. At the same time, the performance of unmixing is not only affected by SVs but also depends on the effective spectral and spatial information. To solve these problems, this study proposed an efficient attention-based convolutional neural network (EACNN) and an efficient convolution block attention module (ECBAM). The EACNN is a two-stream network, which is learned from nearly pure endmembers through an additional network, and the aggregated spectral and spatial information can be obtained effectively with the help of the ECBAM, which can reduce the influence of SVs and improve the performance. The unmixing network helps the whole network to pay attention to meaningful feature information by using efficient channel attention (ECA) and guides the unmixing process by sharing parameters. Experimental results on three HSI datasets showed that the method proposed in this study outperformed other unmixing methods. hyperspectral unmixing; convolutional neural network; endmember bundle; spectral variability; attention mechanism +
Footnote †: journal: Applied  
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Footnote 1: email: _Applied_  
change significantly with changes in the environment, including changes in the illumination and atmosphere, perhaps resulting in estimation errors propagated throughout the unmixing process. Therefore, spectral variability (SV) has attracted wide attention. [PERSON] et al. [13] proposed a dictionary adjustment method to solve the SV problem, where SV is regarded as an endmember dictionary in the spectral library that does not match the observed spectral features. In fact, some SVs are caused by additive perturbations that destroy the original pure endmember spectrum. An interference matrix can be used to model this kind of spectral variation. [PERSON] et al. [14] considered SVs as additional endmember disturbance information and developed a perturbed LMM model (PLMM) on the basis of minimum volume constrained non-negative matrix factorization (MVCNMF) [15]; however, this model lacks specific physical meaning. To clarify the physical meaning, [PERSON] et al. [16] proposed the extended LMM (ELMM), which effectively simulates changes in reflectivity due to changes in lighting by multiplying the diagonal matrix and the endmember. Although the physical meaning of this method is clear, the ELMM model assumes that all wavelengths have a fixed scaling ratio, and thus, this model has some limitations when the endmembers are under the influence of a more complex environment. In a variety of complex hyperspectral scenes, an LMM's data unmixing and reconstruction ability is limited. An NLMM is constructed from an LMM by considering specific nonlinear factors to improve the unmixing performance. Initially, the Hapke model [17], which is based on radiative transfer theory (RTT), was proposed. It uses a mathematical model to express complex nonlinear mixed phenomena and then solves the model. However, it has serious limitations, including difficulties with complex and vegetation-covered scenes. Later, in keeping with the physical meaning of the model, a simplification in the form of the bilinear mixture model (BMM) [18], which can be applied to two-layer mixed scenarios, was proposed. [PERSON] et al. [19] made further improvements, allowing the model to tackle a variety of mixed-material scenarios. However, these models have some limitations. For example, an endmember must be extracted in advance by another algorithm before its abundance can be estimated. This issue leads to many limitations in the event of complex SV scenarios. Data-driven NLMM has also attracted much attention. Unlike the model-based NLMM, the data-driven method does not require that the nonlinear mixed form be known and only needs data to carry out endmember extraction and abundance inversion.