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In multimodal land cover classification (MLCC), a common challenge is theredundancy in data distribution, where irrelevant information from multiplemodalities can hinder the effective integration of their unique features. Totackle this, we introduce the Multimodal Informative Vit (MIVit), a system withan innovative information aggregate-distributing mechanism. This approachredefines redundancy levels and integrates performance-aware elements into thefused representation, facilitating the learning of semantics in both forwardand backward directions. MIVit stands out by significantly reducing redundancyin the empirical distribution of each modality's separate and fused features.It employs oriented attention fusion (OAF) for extracting shallow localfeatures across modalities in horizontal and vertical dimensions, and aTransformer feature extractor for extracting deep global features throughlong-range attention. We also propose an information aggregation constraint(IAC) based on mutual information, designed to remove redundant information andpreserve complementary information within embedded features. Additionally, theinformation distribution flow (IDF) in MIVit enhances performance-awareness bydistributing global classification information across different modalities'feature maps. This architecture also addresses missing modality challenges withlightweight independent modality classifiers, reducing the computational loadtypically associated with Transformers. Our results show that MIVit'sbidirectional aggregate-distributing mechanism between modalities is highlyeffective, achieving an average overall accuracy of 95.56% across threemultimodal datasets. This performance surpasses current state-of-the-artmethods in MLCC. The code for MIVit is accessible athttps://github.com/icey-zhang/MIViT.

Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification