Learning Unified Anchor Graph for Joint Clustering of Hyperspectral and LiDAR Data
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces anchor-based multiview kernel subspace clustering (AMKSC) for scalable remote sensing data analysis. AMKSC efficiently clusters large datasets by learning an anchor graph in kernel space and incorporating spatial regularization.
Area Of Science
- Earth Observation
- Computer Vision
- Data Science
Background
- Joint clustering of multimodal remote sensing (RS) data is challenging for Earth observation.
- Existing multiview subspace clustering methods struggle with large-scale RS datasets due to computational costs.
- Current methods often overlook nonlinear, spatial dependencies, and lack out-of-sample generalization.
Purpose Of The Study
- To develop a scalable and effective framework for joint clustering of multimodal remote sensing data.
- To address the limitations of existing methods in handling large-scale, heterogeneous RS data and out-of-sample instances.
- To improve clustering performance and computational efficiency in remote sensing data analysis.
Main Methods
- Introduced anchor-based multiview kernel subspace clustering with spatial regularization (AMKSC).
- Learned a scalable anchor graph in kernel space, utilizing contributions from each modality.
- Incorporated spatial smoothing for consistency and employed alternating optimization for efficient solving.
- Developed an out-of-sample extension using multiview collaborative representation-based classification.
Main Results
- AMKSC demonstrates superior clustering performance on three real heterogeneous RS datasets.
- The proposed method achieves significantly improved time efficiency compared to state-of-the-art approaches.
- Theoretical analysis confirms the scalability of AMKSC with linear computational complexity.
- The out-of-sample extension effectively handles larger datasets and unseen instances.
Conclusions
- AMKSC offers a novel, unified, and efficient framework for joint clustering of multimodal RS data.
- The method overcomes computational limitations and enhances generalization for remote sensing applications.
- AMKSC provides a significant advancement in scalable and accurate analysis of complex Earth observation data.
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