Abstract:

While traditional unmixing methods and autoencoder-based unmixing networks have improved the unmixing performance by utilizing spatial information,they have not fully explored and leveraged spectral features.The effective integration of spectral features with spatial information could further enhance the unmixing performance.Therefore,an unmixing framework based on a Bidirectional Convolutional Long Short-Term Memory Autoencoder Network(CLAENet) with an innovative network architecture design is proposed.This framework deeply mines spatial features through convolutional layers,while convolutional long short-term memory units are used to fully explore spectral variability and the correlations between bands,effectively processing the sequential information on the spectral dimension for a more accurate and efficient analysis of hyperspectral data.To further distinguish and utilize the specificity of different spectral bands in hyperspectral data,a deep spectral partitioning method is adopted to optimize the network input.An adaptive learning mechanism is employed for refined processing of different spectral regions,enhancing the model's capability to capture complex spectral relationships within hyperspectral data and further improving unmixing performance.Comparative experiments conducted on simulated and multiple real hyperspectral datasets demonstrate that this method outperforms existing methods in terms of unmixing accuracy and model robustness.Notably,it exhibits good generalization and stability when handling complex spectral features of land cover,thus accurately estimating endmembers and abundances.

Key words: hyperspectral imaging, recurrent neural networks, autoencoders, convlstm, deep spectral partitioning