###### Abstract
The kernel method is a representative data-driven method. It projects the obtained original nonlinear data onto a high-dimensional space and then performs linear HU in that space. Relevant algorithms mainly include kernel fully constrained least squares (FCLS) [20; 21] and non-negative matrix factorization (NMF) unmixing based on the kernel model [22; 23]. Recently, deep learning (DL) has developed rapidly, and it has been widely used in computer vision and natural language processing [24; 25]. DL has attracted much attention in terms of HU due to its strong feature representation and learning abilities. Initially, some basic network frameworks were applied to HU [26], but these methods required ground truth or endmember training sets with known abundances, which caused an issue given that ground truth availability is very limited. Autoencoder (AE) networks have been widely used in HU due to their characteristics and good performances [27; 28; 29]. An AE network can reconstruct the output data to find the low-dimensional representation (abundance score) of an HSI. In addition, a convolution neural network (CNN) is able to extract structural features from an HSI, and thus, it is also very suitable for HU tasks. [PERSON] et al. [30] proposed a deep autoencoder network (DAEN) for unmixing hyperspectral data with outliers. In [31], [PERSON] proposed a framework called WU-NET, which was used to deal with SV. In addition, the two-stream autoencoder network (TANET) [32] uses super-pixel segmentation as a preprocessing technique to extract endmember bundles for two-stream autoencoder unmixing. However, during dimensionality reduction, the AE will inevitably lose feature information from an HSI. In [33], an end-to-end pixel-based CNN was proposed to solve the unmixing task, and the multilayer perceptron (MLP) structures were used to obtain the pixel abundance. In [34], [PERSON] et al. used CNNs for HU. Amongthem, a long short-term memory network was better at unmixing than a linear hybrid encoder-decoder method. Attention-based methods originated from natural language processing (NLP). In recent years, attention mechanisms have been used in many fields, such as image classification [35; 36; 37] and target detection [38; 39]. At present, attention mechanisms are shown to play a good role in capturing HSI features. [PERSON] et al. [40] designed a successive pooling attention network for the semantic segmentation of remote sensing images. [PERSON] et al. [41] designed a recurrent thrifty attention network for remote sensing recognition by using a self-attention mechanism. [PERSON] et al. [42] designed a residual network based on an attention mechanism to conduct HU for limited training samples. [PERSON] et al. [43] improved its performance by using a squeeze excitation (SE)-driven attention mechanism to consider the differences between optical detection and ranging LiDAR heights to guide the unmixing process. Attention-based networks have great potential for capturing features, and there is still a lot of room for exploration in this field. Hence, our network optimizes the modeling by considering the HSI feature information in the unmixing process. This study re-examined the limitations of the nonlinear hybrid model and the existing solution mixing schemes to propose workarounds for these shortcomings. By extracting the information from the HSI with physically meaningful endmembers, the attention module can also learn the hyperspectral feature information. Accordingly, an efficient attention-based CNN for HU was proposed in this study. The main contributions of this study are as follows:  
1. This study proposed an efficient attention-based convolutional neural network called the EACNN, which simulates endmembers in a physically meaningful and self-supervised way and captures hyperspectral information effectively, allowing for the HU of complex scenes. 2. Inspired by the attention mechanism approach, an efficient convolution block attention module (ECBAM) for HU was proposed.