MCIB: Multi-modal Complementary Information Bottleneck for Hyperspectral and LiDAR Classification | JoVE Visualize

Area of Science:

Remote Sensing
Computer Vision
Information Theory

Background:

Accurate land use and land cover (LULC) classification relies on fusing multi-modal remote sensing data like hyperspectral imagery (HSI) and light detection and ranging (LiDAR).
Existing fusion methods face challenges with data redundancy and underutilizing cross-modal complementarity due to a lack of unified theoretical frameworks.

Purpose of the Study:

To propose a novel framework, the multi-modal complementary information bottleneck (MCIB), to address data redundancy and complementarity issues in HSI-LiDAR fusion for LULC classification.
To develop a theoretically grounded and computationally feasible approach for learning compact, sufficient, and complementary multi-modal representations.

Main Methods:

Formalized the MCIB objective with structured priors to derive information-theoretic bounds, enabling simultaneous reduction of redundancy and enhancement of complementarity.
Designed an end-to-end variational optimization strategy utilizing a novel supervised conditional InfoNCE (SCInfoNCE) method to optimize conditional mutual information for improved data synergy.
Leveraged existing model components for efficient implementation of the supervised contrastive learning approach.

Main Results:

MCIB framework demonstrated superior performance in HSI-LiDAR data fusion for LULC classification across benchmark datasets.
The proposed method effectively reduces data redundancy while maximizing the complementary information between HSI and LiDAR data.
Achieved significant improvements in classification accuracy compared to existing methods.

Conclusions:

The MCIB framework provides a principled and robust solution for multi-modal representation learning in remote sensing.
This work bridges a theoretical gap in understanding and utilizing cross-modal complementarity for complex heterogeneous data.
The developed approach offers a practical and effective tool for advanced LULC classification using fused HSI and LiDAR data.