In multimodal land cover classification (MLCC), a common challenge is the redundancy in data distribution, where irrelevant information from multiple modalities can hinder the effective integration of their unique features. To tackle this, we introduce the Multimodal Informative Vit (MIVit), a system with an innovative information aggregate-distributing mechanism. This approach redefines redundancy levels and integrates performance-aware elements into the fused representation, facilitating the learning of semantics in both forward and backward directions. MIVit stands out by significantly reducing redundancy in the empirical distribution of each modality's separate and fused features. It employs oriented attention fusion (OAF) for extracting shallow local features across modalities in horizontal and vertical dimensions, and a Transformer feature extractor for extracting deep global features through long-range attention. We also propose an information aggregation constraint (IAC) based on mutual information, designed to remove redundant information and preserve complementary information within embedded features. Additionally, the information distribution flow (IDF) in MIVit enhances performance-awareness by distributing global classification information across different modalities' feature maps. This architecture also addresses missing modality challenges with lightweight independent modality classifiers, reducing the computational load typically associated with Transformers. Our results show that MIVit's bidirectional aggregate-distributing mechanism between modalities is highly effective, achieving an average overall accuracy of 95.56% across three multimodal datasets. This performance surpasses current state-of-the-art methods in MLCC. The code for MIVit is accessible at https://github.com/icey-zhang/MIViT.

多模式土地覆盖分类中，通过引入多模式信息聚合和分布机制的多模式信息子网络(MIVit)来处理数据分布中的冗余问题，通过在水平和垂直维度上提取浅层局部特征和通过远程注意力提取深层全局特征，MIVit显著减少了每种模式的独立特征和融合特征的冗余，并通过相互信息基于信息聚合约束的方法去除冗余信息并保留嵌入特征中的互补信息，同时在多模式特征图之间分布全局分类信息，解决了缺失模态挑战。实验结果表明，MIVit在三个多模式数据集上取得了95.56％的平均整体准确率，超过了现有MLCC方法的最新成果。

多模式信息化ViT：高光谱和LiDAR分类的信息聚合与分配