  Current hyperspectral anomaly detection (HAD) benchmark datasets suffer from
low resolution, simple background, and small size of the detection data. These
factors also limit the performance of the well-known low-rank representation
(LRR) models in terms of robustness on the separation of background and target
features and the reliance on manual parameter selection. To this end, we build
a new set of HAD benchmark datasets for improving the robustness of the HAD
algorithm in complex scenarios, AIR-HAD for short. Accordingly, we propose a
generalized and interpretable HAD network by deeply unfolding a
dictionary-learnable LLR model, named LRR-Net$^+$, which is capable of
spectrally decoupling the background structure and object properties in a more
generalized fashion and eliminating the bias introduced by vital interference
targets concurrently. In addition, LRR-Net$^+$ integrates the solution process
of the Alternating Direction Method of Multipliers (ADMM) optimizer with the
deep network, guiding its search process and imparting a level of
interpretability to parameter optimization. Additionally, the integration of
physical models with DL techniques eliminates the need for manual parameter
tuning. The manually tuned parameters are seamlessly transformed into trainable
parameters for deep neural networks, facilitating a more efficient and
automated optimization process. Extensive experiments conducted on the AIR-HAD
dataset show the superiority of our LRR-Net$^+$ in terms of detection
performance and generalization ability, compared to top-performing rivals.
Furthermore, the compilable codes and our AIR-HAD benchmark datasets in this
paper will be made available freely and openly at
\url{https://sites.google.com/view/danfeng-hong}.
