This article introduces the Autoencoder Graph Ensemble Model (AEGEM), a novel ensemble-based framework designed to enhance performance in both endmember extraction and abundance estimation. In the ...

Through effective preprocessing and the utilization of graph convolutional networks, the GCN-Autoencoder model achieved outstanding results, with an accuracy of 99.70%, an F1-score of 99.97%, and a ...

The architecture of the model is shown in Figure 5. Figure 5. Model architecture diagram of the deep convolutional autoencoder. The input to the model is a 9 × 24 matrix, where 9 represents the 9 ...