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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Spectral Adversarial Feature Learning for Anomaly Detection in Hyperspectral Imagery
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Spectral Adversarial Feature Learning for Anomaly Detection in Hyperspectral Imagery

机译：高光谱图像中异常检测的光谱对抗特征

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摘要

Theoretically, hyperspectral images (HSIs) are capable of providing subtle spectral differences between different materials, but in fact, it is difficult to distinguish between background and anomalies because the samples of anomalous pixels in HSIs are limited and susceptible to background and noise. To explore the discriminant features, a spectral adversarial feature learning (SAFL) architecture is specially designed for hyperspectral anomaly detection in this article. In addition to reconstruction loss, SAFL also introduces spectral constraint loss and adversarial loss in the network with batch normalization to extract the intrinsic spectral features in deep latent space. To further reduce the false alarm rate, we present an iterative optimization approach by a weighted suppression function that depends on the contribution rate of each feature to the detection. In particular, the structure tensor matrix is adopted to adaptively calculate the contribution rate of each feature. Benefiting from these improvements, the proposed method is superior to the typical and state-of-the-art methods either in detection probability or false alarm rate.
机译：理论上，高光谱图像（HSIS）能够在不同材料之间提供微妙的光谱差异，但实际上，难以区分背景和异常，因为HSIS中的异常像素的样本受到限制并且易于背景和噪声。为了探讨判别特征，光谱对抗特征学习（SAFL）架构专为本文中的高光谱异常检测设计。除了重建损失之外，SAFL还引入了网络中的光谱约束损失和具有批量归一化的对抗损失，以提取深度潜空间中的内在光谱特征。为了进一步降低误报率，我们通过加权抑制函数呈现迭代优化方法，这取决于每个特征的贡献率到检测。特别地，采用结构张量矩阵来自适应地计算每个特征的贡献率。受益于这些改进，所提出的方法优于典型的和最先进的方法，无论是在检测概率还是误报率。

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