Real hyperspectral images (HSIs) are ineluctably contaminated by diverse types of noise, which severely limits the image usability. Recently, transfer learning has been introduced in hyperspectral denoising networks to improve model generalizability. However, the current frameworks often rely on image priors and struggle to retain the fidelity of background information. In this article, an unsupervised adaptation learning (UAL)-based hyperspectral denoising network (UALHDN) is proposed to address these issues. The core idea is first learning a general image prior for most HSIs, and then adapting it to a real HSI by learning the deep priors and maintaining background consistency, without introducing hand-crafted priors. Following this notion, a spatial–spectral residual denoiser, a global modeling discriminator, and a hyperspectral discrete representation learning scheme are introduced in the UALHDN framework, and are employed across two learning stages. First, the denoiser and the discriminator are pretrained using synthetic noisy-clean ground-based HSI pairs. Subsequently, the denoiser is further fine-tuned on the real multiplatform HSI according to a spatial–spectral consistency constraint and a background consistency loss in an unsupervised manner. A hyperspectral discrete representation learning scheme is also designed in the fine-tuning stage to extract semantic features and estimate noise-free components, exploring the deep priors specific for real HSIs. The applicability and generalizability of the proposed UALHDN framework were verified through the experiments on real HSIs from various platforms and sensors, including unmanned aerial vehicle-borne, airborne, spaceborne, and Martian datasets. The UAL denoising scheme shows a superior denoising ability when compared with the state-of-the-art hyperspectral denoisers.

中文翻译：

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面向真实多平台高光谱图像去噪的无监督适应学习


真实的高光谱图像 （HSI） 不可避免地受到各种类型噪声的污染，这严重限制了图像的可用性。最近，在高光谱去噪网络中引入了迁移学习，以提高模型的泛化性。但是，当前的框架通常依赖于图像先验，并且难以保持背景信息的保真度。在本文中，提出了一种基于无监督适应学习 （UAL） 的高光谱去噪网络 （UALHDN） 来解决这些问题。核心思想是首先学习大多数 HSI 的一般图像先验，然后通过学习深度先验并保持背景一致性来将其适应真实的 HSI，而无需引入手工制作的先验。遵循这一概念，在 UALHDN 框架中引入了空间-频谱残差降噪器、全局建模判别器和高光谱离散表示学习方案，并在两个学习阶段中使用。首先，使用合成的噪声干净地面 HSI 对对降噪器和判别器进行预训练。随后，根据空间-光谱一致性约束和无监督方式在真实的多平台 HSI 上进一步微调降噪器。在微调阶段还设计了一种高谱离散表示学习方案，以提取语义特征和估计无噪声分量，探索真实 HSI 特有的深度先验。通过对来自各种平台和传感器的真实 HSI 进行实验，验证了所提出的 UALHDN 框架的适用性和泛化性，包括无人机载、机载、星载和火星数据集。 与最先进的高光谱降噪器相比，UAL 降噪方案显示出卓越的降噪能力。