多视图Transformer：重新思考高光谱图像分类中的空间信息
Multiview Transformer: Rethinking Spatial Information in Hyperspectral Image Classification
AI总结：
针对高光谱图像分类中的空间过拟合问题，提出多视图Transformer，通过多视图主成分分析、光谱编码器-解码器和空间池化标记化Transformer提取鲁棒特征，在三个数据集上优于现有方法。
摘要：
在高光谱图像（HSI）中，识别每个像素的土地覆盖类别依赖于光谱和空间信息。利用具有特定块大小的HSI立方体来提取中心像素的空间-光谱特征表示。在本文中，我们研究了HSI立方体中可能记录的场景特定但非本质的相关性。这种额外信息提高了模型在现有HSI数据集上的性能，并使得难以正确评估模型的能力。我们将此问题称为空间过拟合问题，并采用严格的实验设置来避免它。我们进一步提出了一种用于HSI分类的多视图Transformer，它由多视图主成分分析（MPCA）、光谱编码器-解码器（SED）和空间池化标记化Transformer（SPTT）组成。MPCA通过构建光谱多视图观测并对每个视图数据应用PCA来提取低维视图表示，从而对HSI进行降维。视图表示的组合，称为多视图表示，是MPCA的降维输出。为了聚合多视图信息，引入了在光谱维度上具有U形的全卷积SED来提取多视图特征图。SPTT使用空间池化标记化策略将多视图特征转换为标记，并学习用于土地覆盖识别的鲁棒且具有判别性的空间-光谱特征。使用线性分类器进行分类。在三个具有严格设置的HSI数据集上的实验证明了所提出的多视图Transformer相对于最先进方法的优越性。
英文摘要：
Identifying the land cover category for each pixel in a hyperspectral image (HSI) relies on spectral and spatial information. An HSI cuboid with a specific patch size is utilized to extract spatial-spectral feature representation for the central pixel. In this article, we investigate that scene-specific but not essential correlations may be recorded in an HSI cuboid. This additional information improves the model performance on existing HSI datasets and makes it hard to properly evaluate the ability of a model. We refer to this problem as the spatial overfitting issue and utilize strict experimental settings to avoid it. We further propose a multiview transformer for HSI classification, which consists of multiview principal component analysis (MPCA), spectral encoder-decoder (SED), and spatial-pooling tokenization transformer (SPTT). MPCA performs dimension reduction on an HSI via constructing spectral multiview observations and applying PCA on each view data to extract low-dimensional view representation. The combination of view representations, named multiview representation, is the dimension reduction output of the MPCA. To aggregate the multiview information, a fully-convolutional SED with a U-shape in spectral dimension is introduced to extract a multiview feature map. SPTT transforms the multiview features into tokens using the spatial-pooling tokenization strategy and learns robust and discriminative spatial-spectral features for land cover identification. Classification is conducted with a linear classifier. Experiments on three HSI datasets with rigid settings demonstrate the superiority of the proposed multiview transformer over the state-of-the-art methods.
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。
