Background Reconstruction via 3D-Transformer Network for Hyperspectral Anomaly Detection
Background Reconstruction via 3D-Transformer Network for Hyperspectral Anomaly Detection
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DOI：
10.3390/rs15184592
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Ziyu Wu;Bin Wang
中科院分区：
文献类型：
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作者：
Ziyu Wu;Bin Wang
近年来，基于自动编码器(AE)的高光谱图像异常检测方法被广泛提出，但其重建精度容易受到异常和噪声的影响。此外，这些基于声发射的异常检测器只是将每个像素压缩成一个较低维度的隐藏层，然后对其进行重建，没有考虑像素之间的空间特性。针对上述问题，提出了一种基于3D-Transform(3DTR)网络的背景重构框架，用于HSIS中的异常检测。在合成和实际高光谱数据集上的实验结果表明，该3DTR网络综合考虑了像元之间的空间相关性和HSI光谱波段之间的光谱相似性，能够有效地检测出大部分异常。此外，由于采用了预检测过程和本文提出的新的补丁生成方法，该方法比传统的和最新的(包括基于模型的和基于声发射的)异常检测器具有更少的虚警。此外，两个烧蚀实验充分验证了所提出方法中各部分的有效性。
Recently, autoencoder (AE)-based anomaly detection approaches for hyperspectral images (HSIs) have been extensively proposed; however, the reconstruction accuracy is susceptible to the anomalies and noises. Moreover, these AE-based anomaly detectors simply compress each pixel into a hidden-layer with a lower dimension and then reconstruct it, which does not consider the spatial properties among pixels. To solve the above issues, this paper proposes a background reconstruction framework via a 3D-transformer (3DTR) network for anomaly detection in HSIs. The experimental results on both synthetic and real hyperspectral datasets demonstrate that the proposed 3DTR network is able to effectively detect most of the anomalies by comprehensively considering the spatial correlations among pixels and the spectral similarity among spectral bands of HSIs. In addition, the proposed method exhibits fewer false alarms than both traditional and state-of-the-art (including model-based and AE-based) anomaly detectors owing to the adopted pre-detection procedure and the proposed novel patch-generation method in this paper. Moreover, two ablation experiments adequately verified the effectiveness of each component in the proposed method.