## 3 Proposed Method
### Unmixing Network  
The UN structure is roughly similar to that of the EN because the UN and EN can share weights in the unmixing process, allowing the attributes of the endmembers to be fully taken into account in the unmixing process. The UN is made up of two similar parts: the unmixing and reconstruction modules.  
In order to effectively share the information obtained by the EN with the UN, the sharing strategy involves learning in a partially shared fashion following the extraction of spectral feature information.  
Due to the existence of many different SVs in hyperspectral data, linear activation functions cannot be used in unmixing. At the same time, a linearization operation cannot fully reproduce the original spectral details of an HSI with complex SVs. Therefore, across the whole network, a large number of nonlinear activation functions are used. The UN learns by sharing some parameters. Note that the ECBAM is used in the EN because the endmember bundle input processes the spatial information. With a certain degree of aggregation, the extraction of spatial feature information for the EN is efficient, and to a certain extent, the extracted information can be guaranteed to promote network learning. At this time, the UN's extraction of spatial information from the original HSI will add factors that are irrelevant to the unmixing and may even affect the unmixing performance. Thus, the proposed method favors a balance between accuracy and efficiency to further improve the estimation of abundances. The UN and EN have similar settings; for specific settings, please refer to the EN outlined above.  
Through its extraction and reconstruction of the detailed features of the HSI, the network can obtain satisfactory unmixing results. It should be noted that HSIs contain a wider range of information and a larger amount of data than natural images of the same size; therefore, lightweight modules can be used in multi-scale feature extraction. Only a few parameters can effectively improve the network performance.  
The abundances of an unmixing module can be derived directly through \(\mathrm{a_{soft}}\). The optimization of the UN reconstruction module \(\mathrm{L_{UN}}\) is obtained by minimizing the reconstruction error:  
\[\mathrm{L_{UN}}=\min_{\mathrm{W_{UN}B_{UN}}}\left\|\mathrm{X-r_{m}}(\mathrm{g _{u}}(\mathrm{w_{UN}},\mathrm{B_{UN}},\mathrm{X}))\right\|_{\mathrm{F}}^{2} \tag{15}\]  
where \(\mathrm{r_{m}}\) and \(\mathrm{g_{u}}\) correspond to the mapping functions of the unmixing and reconstruction modules with respect to the weights \(\mathrm{W_{UN}}\) and biases \(\mathrm{B_{UN}}\) of the unmixing module, respectively. The overall EACNN loss can be expressed as follows:  
\[\mathrm{Loss}=\mathrm{L_{EN}}+\mathrm{L_{UN}} \tag{16}\]As shown in [46], the endmembers can be obtained indirectly. Further, the endmembers (\(\text{E}=\{\text{e}_{\text{i}}\}_{\text{i=1}}^{\text{C}}\)) can be estimated via a simple linear model when abundances are known:  
\[\text{min}_{\text{E}}\|\text{X}-\text{E}\text{y}\|_{\text{F}}^{2}\quad\text{s.t. }\text{E}\geq 0 \tag{17}\]  
It should be noted that our model uses a large number of nonlinear activation functions, while the linear model is simply convenient for visualizing endmembers and comparing the endmembers to reference endmembers. The specific network parameters are shown in Table 2.