all dataset

Hyper-spectral unmixing is a technique to estimate the abundances of different endmembers in each mixed pixel of remote sensing images. Deep learning has made significant progress in this area, offering automatic feature extraction, nonlinear pattern recognition, and end-to-end solutions. However, existing deep learning models have not fully utilized the spectral information of endmembers, leading to insufficient data mining. We propose a pixel-based Additive Attention Neural Network (AANN) that uses endmember spectral feature vectors as auxiliary data for training, which helps improve the accuracy of mixed pixel decomposition. Additionally, to validate the influence of adjacent pixels, we developed a spatial-based AANN that adds a convolutional layer to extract spatial features, exploring the endmember decomposition accuracy for various window sizes. Experimental results show: 1. The pixel-based AANN significantly outperformed traditional machine learning and model-based methods; 2. The spatial-based AANN, which incorporated features of neighboring pixels, performed nearly as well as the pixel-based AANN on the Jasper Ridge dataset, but showed a notable 40.5% improvement on the urban dataset; 3. The spatial-based AANN showed the best effect with a 3 × 3 window size. These results indicate that adding spectral information and adjacent pixel information can effectively improve unmixing accuracy.