Hyperspectral remote sensing images (HSIs) have been applied in urban planning, environmental monitoring, and other fields. However, they are susceptible to noise interference, such as Gaussian noise, stripe, and mixed noises, from various factors in the imaging process, which greatly limits their applications. Although previous efforts to improve HSI quality have achieved remarkable results, there are still many challenges to be solved. To avoid the poor generalization ability and improve the stripe removal performance of the network in real scenarios, in this article, we proposed a novel deep learning model (Translution-SNet) for HSI stripe noise removal based on a semisupervised training strategy that applies a convolution and transformer for feature extraction. Moreover, we used an unbiased estimation method to calculate the loss function of the unsupervised part from noisy data without a clean image. The semisupervised method improved the ability of Translution-SNet to deal with various complex stripe noises during stripe removal and strengthened its robustness and generalization ability. Our experimental results showed that Translution-SNet could robustly handle stripe noise of images with different loads and achieve satisfactory results, proving its feasibility and effectiveness. In addition, Translution-SNet showed good generalization ability.

中文翻译：

---

Translution-SNet：基于Transformer和CNN的半监督高光谱图像条纹噪声去除


高光谱遥感图像（HSI）已应用于城市规划、环境监测等领域。然而，它们在成像过程中容易受到各种因素的噪声干扰，例如高斯噪声、条纹和混合噪声，这极大地限制了它们的应用。尽管前期提升恒生指数质量的努力取得了显著成效，但仍存在许多挑战有待解决。为了避免泛化能力差并提高网络在实际场景中条纹去除性能，在本文中，我们提出了一种基于应用卷积的半监督训练策略的 HSI 条纹噪声去除深度学习模型（Translution-SNet）和用于特征提取的变压器。此外，我们使用无偏估计方法从没有干净图像的噪声数据中计算无监督部分的损失函数。半监督方法提高了Translution-SNet在条纹去除过程中处理各种复杂条纹噪声的能力，增强了其鲁棒性和泛化能力。我们的实验结果表明，Translution-SNet能够鲁棒地处理不同负载图像的条纹噪声，并取得满意的结果，证明了其可行性和有效性。此外，Translution-SNet表现出良好的泛化能力。