Hyperspectral target detection is challenging in scenarios where spectral variability is high due to noise, spectral redundancy, and mixing. In addition, this spectral variability also creates the need for target detection algorithms to be robust against variations in the detection threshold. To overcome these challenges, this paper proposes a novel two-stage process for improved target detection in hyperspectral data. In the first stage, coarse detection is performed using a detector with a high probability of detection to identify background samples. These background samples are then used for background learning using an adversarial autoencoder (AAE) network, having spectral angle mapper (SAM) and Huber loss functions to minimize the impact of target pixels’ contamination. In the second stage, an inference is made using the spectral difference between the hyperspectral data and the output of the learned background model, which helps in reducing the false alarm rate of the first stage. The proposed approach is compared with seven other target detection techniques using multiple datasets and evaluated through several metrics, such as the area under the curve (AUC) and signal-to-noise probability ratio (SNPR). Results reveal that the proposed technique outperforms other detectors in terms of SNPR, indicating improved target detectability, background suppressibility, and more tolerance to variations in the detection threshold.

中文翻译：

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使用自监督背景学习的高光谱目标检测

在由于噪声、光谱冗余和混合而导致光谱变异性较高的情况下，高光谱目标检测具有挑战性。此外，这种光谱变化还需要目标检测算法能够针对检测阈值的变化保持鲁棒性。为了克服这些挑战，本文提出了一种新颖的两阶段过程，用于改进高光谱数据中的目标检测。在第一阶段，使用具有高检测概率的检测器进行粗检测以识别背景样本。然后，使用对抗性自动编码器 (AAE) 网络将这些背景样本用于背景学习，该网络具有光谱角度映射器 (SAM) 和 Huber 损失函数，以最大限度地减少目标像素污染的影响。在第二阶段，利用高光谱数据和学习背景模型的输出之间的光谱差异进行推断，这有助于降低第一阶段的误报率。将所提出的方法与使用多个数据集的其他七种目标检测技术进行比较，并通过多个指标进行评估，例如曲线下面积（AUC）和信噪比（SNPR）。结果表明，所提出的技术在 SNPR 方面优于其他检测器，这表明目标可检测性、背景抑制性和对检测阈值变化的容忍度有所提高。