Recently, Autoencoders (AEs) have demonstrated remarkable performance in the field of hyperspectral anomaly detection, owing to their powerful capability in handling high-dimensional data. However, they often overlook the inherent global distribution characteristics and long-range dependencies in hyperspectral images (HSI). This oversight makes it challenging to accurately characterize and describe boundaries between different backgrounds and anomalies in complex HSI, thereby affecting detection accuracy. To address this issue, a robust multi-stage progressive autoencoder for hyperspectral anomaly detection (RMSAD) is proposed. Initially, a progressive multi-stage learning framework based on convolutional autoencoders is employed. This framework incrementally reveals and integrates deep contextual features along with their long-range dependencies in HSI, aiming to accurately characterize the background and anomalies. Subsequently, an innovative multi-scale fusion strategy is introduced at the intersections of each stage, reinforcing the learning and representation of background and global spatial details across multiple stages. Finally, by collectively extracting abnormal spatial information across stages, effectively reducing the tendency of autoencoders to reconstruct anomalies. This ensures the efficient restoration and replication of global textural details in HSI. The experimental results on the six HSI datasets demonstrate that the proposed RMSAD is superior to other state-of-the-art methods.

中文翻译：

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用于高光谱异常检测的稳健多级逐行自动编码器


最近，自动编码器 （AE） 凭借其处理高维数据的强大能力，在高光谱异常检测领域表现出卓越的性能。然而，它们经常忽视高光谱影像 （HSI） 中固有的全局分布特征和长期依赖性。这种疏忽使得准确表征和描述复杂 HSI 中不同背景和异常之间的边界变得具有挑战性，从而影响检测准确性。针对这一问题，该文提出一种用于高光谱异常检测的鲁棒多级渐进式自动编码器（RMSAD）。最初，采用基于卷积自动编码器的渐进式多阶段学习框架。该框架逐步揭示和集成深度上下文特征及其在 HSI 中的长期依赖关系，旨在准确描述背景和异常。随后，在每个阶段的交叉点引入创新的多尺度融合策略，加强了跨多个阶段对背景和全局空间细节的学习和表示。最后，通过跨阶段集体提取异常空间信息，有效降低了自编码器重建异常的趋势。这确保了 HSI 中全局纹理细节的高效恢复和复制。在六个 HSI 数据集上的实验结果表明，所提出的 RMSAD 优于其他最先进的方法。