BSDM：超分辨率异常检测的背景抑制扩散模型
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection
AI总结：
BSDM通过设计伪背景噪声和改进扩散模型推断过程，实现无需标注样本的超光谱异常检测背景抑制。
摘要：
超光谱异常检测（HAD）广泛应用于地球观测和深空探测。HAD面临的主要挑战是输入超光谱图像（HSI）的复杂背景，导致异常在背景中被混淆。另一方面，HSI缺乏标注样本导致现有HAD方法泛化能力差。本文首次尝试研究一个无需标注样本的新且可泛化的背景学习问题。我们提出了一种新的解决方案BSDM（背景抑制扩散模型），可以同时学习潜在背景分布并泛化到不同数据集以抑制复杂背景。其特点有三个方面：（1）针对HSI的复杂背景，我们设计伪背景噪声，并利用扩散模型（DM）学习其中的潜在背景分布。（2）针对泛化问题，我们应用统计偏移模块，使BSDM适应不同领域的数据集而无需标注样本。（3）为实现背景抑制，我们创新性地改进了DM的推断过程，通过将原始HSI输入去噪网络，去除背景作为噪声。我们的工作为HAD开辟了新的背景抑制途径，可以在无需人工标注数据的前提下提高HAD性能。对四种HAD方法在多个真实HSI数据集上的评估和泛化实验验证了所提方法的上述三个独特特性。代码可在https://github.com/majitao-xd/BSDM-HAD获取。
英文摘要：
Hyperspectral anomaly detection (HAD) is widely used in Earth observation and deep space exploration. A major challenge for HAD is the complex background of the input hyperspectral images (HSIs), resulting in anomalies confused in the background. On the other hand, the lack of labeled samples for HSIs leads to poor generalization of existing HAD methods. This paper starts the first attempt to study a new and generalizable background learning problem without labeled samples. We present a novel solution BSDM (background suppression diffusion model) for HAD, which can simultaneously learn latent background distributions and generalize to different datasets for suppressing complex background. It is featured in three aspects: (1) For the complex background of HSIs, we design pseudo background noise and learn the potential background distribution in it with a diffusion model (DM). (2) For the generalizability problem, we apply a statistical offset module so that the BSDM adapts to datasets of different domains without labeling samples. (3) For achieving background suppression, we innovatively improve the inference process of DM by feeding the original HSIs into the denoising network, which removes the background as noise. Our work paves a new background suppression way for HAD that can improve HAD performance without the prerequisite of manually labeled data. Assessments and generalization experiments of four HAD methods on several real HSI datasets demonstrate the above three unique properties of the proposed method. The code is available at https://github.com/majitao-xd/BSDM-HAD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
