发表机构
LTCI, Télécom Paris, Institut Polytechnique de Paris（LTCI，巴黎高等电信学院，巴黎综合理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文基准测试了高光谱基础模型在解混任务上的性能，发现它们能达到最先进水平，并比较了特征上采样方法，证明简单方法即可有效解决分辨率损失问题。
AI 中文摘要
近期，多个专门用于高光谱图像的基础模型已被公开。这些模型在大规模无标注数据集上训练，并在许多高光谱成像任务（如分类或去噪）中表现出强大的性能。尽管如此，它们在高光谱解混——即分离高光谱图像中重叠材料混合光谱的任务——中的表现仍研究不足。这部分可能是因为大多数模型依赖视觉变换器骨干网络，包括分块处理，导致特征分辨率问题。虽然高光谱解混本身已因高光谱图像的低分辨率而困难，但分块步骤可能使问题更加不适定。因此，在本工作中，我们旨在回答两个问题：1）基础模型在高光谱解混中表现如何？2）如何解决特征级别的分辨率损失？为回答第一个问题，我们对基础模型进行解混基准测试，表明它们在四个高光谱解混数据集上能达到最先进的性能。为回答第二个问题，我们比较了几种特征上采样方法，并实证表明使用简单方法即可获得高性能结果。代码可在 https://gitlab.telecom-paris.fr/ring/hfm-hsu.git 获取。
英文摘要
Several foundation models dedicated to hyperspectral images have recently been made available. These models are trained on large unlabeled datasets and exhibit strong performance on many hyperspectral imaging tasks, such as classification or denoising. Nonetheless, their performance for hyperspectral unmixing -- the task of separating mixed spectra of overlapping materials in a hyperspectral image -- remain understudied. This might partly be due to the fact that most of them rely on vision transformer backbones, including patchification, leading to a feature resolution problem. While hyperspectral unmixing already arises from the low resolution of hyperspectral images, this patchification step potentially makes the problem even more ill-posed. Therefore, in this work, we aim to answer two questions: 1) how do foundation models perform in hyperspectral unmixing?; 2) how to tackle the feature-level loss of resolution? To answer the first question, we benchmark foundation models for unmixing, showing that they can reach state-of-the-art performance on four hyperspectral unmixing datasets. To answer the second question, we compare several feature upsampling approaches and empirically show that using a simple one can lead to high performance results. The code is available at https://gitlab.telecom-paris.fr/ring/hfm-hsu.git.
