Neural Architecture Search With Spatial-Spectral Attention for Higher-Order Nonlinear Hyperspectral Unmixing

The extended multilinear mixing (EMLM) model has garnered considerable attention due to its proficiency in accurately characterizing intricate nonlinear mixing processes in hyperspectral unmixing (HU). However, current deep learning (DL) approaches leveraging EMLM for HU heavily relies on the meticulous design of efficient deep autoencoder (AE) network architectures, a process that is both time-consuming and labor-intensive, requiring profound expertise and extensive design experience. This challenge is particularly pronounced when confronted with mixed phenomena exhibiting varying nonlinear intensities across diverse spectral bands, rendering manual network architecture design to accommodate all bands exceedingly difficult. To address the issue, this study proposes an EMLM HU model based on neural architecture search (NAS) with spatial-spectral attention. The main contributions are threefold: 1) NAS is pioneeringly integrated into HU based on EMLM, enabling adaptive modeling of the intricate relationships among endmembers, abundances, and transition probability parameters; 2) a spectral-spatial attention-guided large-scale search space is designed, incorporating a diverse array of multiscale convolutional operations tailored to the variability in scattering degree across hyperspectral remote sensing image bands. Additionally, a NAS acceleration strategy inspired by sparse coding is employed to alleviate the search time burden stemming from the expanded search space; and 3) a hybrid loss function combining linear reconstruction loss with multilinear reconstruction spectral angle distance (SAD) is formulated to balance the influence of linear components during the unmixing process, enhancing convergence speed and endmember accuracy. Extensive experimental validation on both synthetic and real datasets demonstrates the significant performance advantages of the proposed method, underscoring its potential to revolutionize HU. The code is available at https://github.com/Appe-hub/HNU-NAS.