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

TL;DR

This paper introduces MIVit, a novel multimodal Transformer-based system that reduces redundancy and enhances feature integration in hyperspectral and LiDAR land cover classification, achieving state-of-the-art accuracy.

Contribution

The paper presents a new information aggregation and distribution mechanism within a Transformer framework for improved multimodal land cover classification.

Findings

Achieves 95.56% accuracy on three datasets.

Outperforms existing state-of-the-art methods.

Effectively reduces redundancy in multimodal features.

Abstract

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.