Weakly Supervised Low-Rank Representation for Hyperspectral Anomaly Detection
提出一种弱监督低秩表示方法，将深度学习高光谱异常检测转化为低秩优化问题，无需先验信息且不受标注限制，通过字典低秩表示和初始检测结果共同实现异常检测，性能优于现有方法。
In this article, we propose a weakly supervised low-rank representation (WSLRR) method for hyperspectral anomaly detection (HAD), which formulates deep learning-based HAD into a low-lank optimization problem not only characterizing the complex and diverse background in real HSIs but also obtaining relatively strong supervision information. Different from the existing unsupervised and supervised methods, we first model the background in a weakly supervised manner, which achieves better performance without prior information and is not restrained by richly correct annotation. Considering reconstruction biases introduced by the weakly supervised estimation, LRR is an effective method for further exploring the intricate background structures. Instead of directly applying the conventional LRR approaches, a dictionary-based LRR, including both observed training data and hidden learned data drawn by the background estimation model, is proposed. Finally, the derived low-rank part and sparse part and the result of the initial detection work together to achieve anomaly detection. Comparative analyses validate that the proposed WSLRR method presents superior detection performance compared with the state-of-the-art methods.