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Multiorder Interaction Information Embedding-Based Multiview Fusion-Aided Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters ( IF 4.8 ) Pub Date : 2022-08-17 , DOI: 10.1109/lgrs.2022.3199607
Xuefei Li 1 , Weijia Cao 2 , Kai Zhang 3 , Baodi Liu 4 , Dapeng Tao 5 , Weifeng Liu 4
Affiliation  

Hyperspectral images (HSIs) are obtained from hyperspectral imaging sensors, which capture information in hundreds of spectral bands of objects. However, how to take full advantage of spatial and spectral information from many spectral bands to improve the performance of HSI classification remains an open question. Many HSI classification works have recently been reported by employing multiview learning (MVL) algorithms that can fully use complementary information between different view features and thus have received widespread attention. This letter proposes a multiview fusion network based on multiorder interaction information embedding for HSI classification. First, the correlation matrix between spectral bands is used to divide the original data into multiple subsets as local views. The subset after the segmented-PCA process is used as the global view. Second, the features of different views are extracted separately using a feature extraction network and mapped to the same dimension. Prefusion is achieved by multiorder interaction of various view features. Finally, loss-weighted fusion is applied to each view according to its contribution to the classification task. To evaluate the effectiveness of the proposed method, complete experiments were conducted on three commonly used HSI datasets, namely Pavia University, Houston 2013, and Houston 2018. The experimental results demonstrate that the proposed method improves the classification performance of existing feature extraction networks and is more competitive with other methods in the field.

中文翻译:

基于多阶交互信息嵌入的多视图融合辅助高光谱图像分类

高光谱图像 (HSI) 是从高光谱成像传感器获得的,该传感器捕获数百个物体光谱带中的信息。然而,如何充分利用来自多个光谱波段的空间和光谱信息来提高 HSI 分类的性能仍然是一个悬而未决的问题。许多 HSI 分类工作最近被报道采用多视图学习 (MVL) 算法,该算法可以充分利用不同视图特征之间的互补信息,因此受到广泛关注。这封信提出了一个基于多阶交互信息嵌入的多视图融合网络,用于 HSI 分类。首先,光谱带之间的相关矩阵用于将原始数据划分为多个子集作为局部视图。分段 PCA 处理后的子集用作全局视图。其次,使用特征提取网络分别提取不同视图的特征并映射到同一维度。预融合是通过各种视图特征的多阶交互来实现的。最后,根据每个视图对分类任务的贡献,将损失加权融合应用于每个视图。为了评估所提方法的有效性,在三个常用的 HSI 数据集上进行了完整的实验,即 Pavia University、Houston 2013 和 Houston 2018。实验结果表明,所提方法提高了现有特征提取网络的分类性能,并且是与该领域的其他方法相比更具竞争力。使用特征提取网络分别提取不同视图的特征并映射到同一维度。预融合是通过各种视图特征的多阶交互来实现的。最后,根据每个视图对分类任务的贡献,将损失加权融合应用于每个视图。为了评估所提方法的有效性,在三个常用的 HSI 数据集上进行了完整的实验,即 Pavia University、Houston 2013 和 Houston 2018。实验结果表明,所提方法提高了现有特征提取网络的分类性能,并且是与该领域的其他方法相比更具竞争力。使用特征提取网络分别提取不同视图的特征并映射到同一维度。预融合是通过各种视图特征的多阶交互来实现的。最后,根据每个视图对分类任务的贡献,将损失加权融合应用于每个视图。为了评估所提方法的有效性,在三个常用的 HSI 数据集上进行了完整的实验,即 Pavia University、Houston 2013 和 Houston 2018。实验结果表明,所提方法提高了现有特征提取网络的分类性能,并且是与该领域的其他方法相比更具竞争力。根据对分类任务的贡献,将损失加权融合应用于每个视图。为了评估所提方法的有效性,在三个常用的 HSI 数据集上进行了完整的实验,即 Pavia University、Houston 2013 和 Houston 2018。实验结果表明,所提方法提高了现有特征提取网络的分类性能,并且是与该领域的其他方法相比更具竞争力。根据对分类任务的贡献,将损失加权融合应用于每个视图。为了评估所提方法的有效性,在三个常用的 HSI 数据集上进行了完整的实验,即 Pavia University、Houston 2013 和 Houston 2018。实验结果表明,所提方法提高了现有特征提取网络的分类性能,并且是与该领域的其他方法相比更具竞争力。
更新日期:2022-08-17
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