A Biobjective Model-Driven Autocoder for Blind Hyperspectral Unmixing

Hyperspectral unmixing (HU) decomposes hyperspectral images (HSIs) into pure spectral signatures (endmembers) and their proportions (abundances). Existing deep learning methods for this task predominantly focus on either linear or nonlinear relationships, often struggling to achieve an optimal equilibrium between the robustness of linear estimations and the precision of nonlinear models. Furthermore, these methods often neglect the integration of spatial information and the interpretability of the results. This article proposes a biobjective model-driven autoencoder network for blind HU that simultaneously addresses both linear and nonlinear relationships. By combining linear and nonlinear kernel models within a model-driven deep learning framework, we aim to enhance the interpretability of the results. To effectively capture spatial information, we introduce an adaptive composite kernel (ACK) that integrates traditional and spatial-spectral kernels (SSKs), using a dynamic convolution-like mechanism to optimize their respective weights. Our method establishes linear and nonlinear reconstruction losses, enabling the simultaneous estimation of endmembers from both types of relationships, thus capturing the complex and high-dimensional data structures inherent in HSIs. Experiments on real and synthetic datasets demonstrate the superiority of the proposed method.