In this paper, we introduce a new approach to address the challenge of generalization in hyperspectral anomaly detection (AD). Our method eliminates the need for adjusting parameters or retraining on new test scenes as required by most existing methods. Employing an image-level training paradigm, we achieve a general anomaly enhancement network for hyperspectral AD that only needs to be trained once. Trained on a set of anomaly-free hyperspectral images with random masks, our network can learn the spatial context characteristics between anomalies and background in an unsupervised way. Additionally, a plug-and-play model selection module is proposed to search for a spatial-spectral transform domain that is more suitable for AD task than the original data. To establish a unified benchmark to comprehensively evaluate our method and existing methods, we develop a large-scale hyperspectral AD dataset (HAD100) that includes 100 real test scenes with diverse anomaly targets. In comparison experiments, we combine our network with a parameter-free detector and achieve the optimal balance between detection accuracy and inference speed among state-of-the-art AD methods. Experimental results also show that our method still achieves competitive performance when the training and test set are captured by different sensor devices. Our code is available at https://github.com/ZhaoxuLi123/AETNet.

翻译：本文提出了一种新方法来解决高光谱异常检测中的泛化挑战。该方法消除了大多数现有方法在新测试场景中需要调整参数或重新训练的需求。通过采用图像级训练范式，我们实现了一个只需训练一次的通用异常增强网络用于高光谱异常检测。该网络在包含随机掩码的无异常高光谱图像集上训练，能够以无监督方式学习异常与背景之间的空间上下文特征。此外，我们提出了一种即插即用的模型选择模块，用于搜索比原始数据更适合异常检测任务的空间-光谱变换域。为建立统一基准以全面评估本方法及现有方法，我们开发了一个大规模高光谱异常检测数据集（HAD100），该数据集包含100个具有多样异常目标的真实测试场景。在对比实验中，我们将所提网络与无参数检测器结合，在检测精度与推理速度之间取得了当前最优异常检测方法中的最佳平衡。实验结果表明，即使训练集与测试集由不同传感器设备采集，本方法仍能获得具有竞争力的性能。我们的代码已开源至https://github.com/ZhaoxuLi123/AETNet。