Data augmentation plays a crucial role in improving the data efficiency of reinforcement learning (RL). However, the generation of high-quality augmented data remains a significant challenge. To overcome this, we introduce ACAMDA (Adversarial Causal Modeling for Data Augmentation), a novel framework that integrates two causality-based tasks: causal structure recovery and counterfactual estimation. The unique aspect of ACAMDA lies in its ability to recover temporal causal relationships from limited non-expert datasets. The identification of the sequential cause-and-effect allows the creation of realistic yet unobserved scenarios. We utilize this characteristic to generate guided counterfactual datasets, which, in turn, substantially reduces the need for extensive data collection. By simulating various state-action pairs under hypothetical actions, ACAMDA enriches the training dataset for diverse and heterogeneous conditions. Our experimental evaluation shows that ACAMDA outperforms existing methods, particularly when applied to novel and unseen domains.

Target detection is an important research topic in hyperspectral image processing, and has been the focus of research of many scholars. Compared with many target detection methods, the subspace target detection method is superior. This paper proposes a hyperspectral subspace target detection method LBSE-AMUSE based on the Local Background Subspace Estimation (LBSE) method and the AMUSE algorithm (Algorithm for Multiple Signals Extraction). HyMap airborne hyperspectral remote sensing data is used as the data source, and the performance between the LBSE and LBSE-AMUSE methods is compared by using quantitative indicators such as false alarm rate, detection rate and ROC curve. The experimental results show that the LBSE-AMUSE method has a simple structure, short running time and low false alarm rate compared with the LBSE method, which can suppress the background highlighting target to some extent.