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.

中文翻译：

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用于高光谱图像异常检测的光谱对抗性特征学习

理论上，高光谱图像 (HSI) 能够提供不同材料之间细微的光谱差异，但实际上，由于 HSI 中异常像素的样本有限且容易受到背景和噪声的影响，因此很难区分背景和异常。为了探索判别特征，本文专门为高光谱异常检测设计了光谱对抗性特征学习（SAFL）架构。除了重构损失，SAFL 还在网络中引入光谱约束损失和对抗性损失，通过批量归一化来提取深层潜在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提出了一种迭代优化方法，该方法取决于每个特征对检测的贡献率。特别是采用结构张量矩阵自适应计算每个特征的贡献率。受益于这些改进，所提出的方法在检测概率或误报率方面均优于典型和最先进的方法。