Mutual channels loss and channel-wise attention aided convolutional autoencoder for hyperspectral image unmixing

Hyperspectral imaging is a valuable tool for analyzing and understanding remote sensing data. Our research paper presents a pioneering method for hyperspectral unmixing through the utilization of a convolutional autoencoder (CAE). To train the CAE, our approach incorporates mutual channels loss (MCL) as a loss function, and we implement channel-wise attention within the CAE architecture. Furthermore, we conduct experiments using two datasets, namely the Samson and Apex hyperspectral datasets, to compare the outcomes of our approach against those achieved by state-of-the-art methods. Our findings demonstrate that our suggested approach achieves significant improvements in terms of accuracy, efficiency and being robust as compared to existing methods. These results highlight the potential of our approach for improving hyperspectral unmixing in a range of remote sensing applications.