CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences (1812.11501v2)

Abstract: With a large amount of open satellite multispectral imagery (e.g., Sentinel-2 and Landsat-8), considerable attention has been paid to global multispectral land cover classification. However, its limited spectral information hinders further improving the classification performance. Hyperspectral imaging enables discrimination between spectrally similar classes but its swath width from space is narrow compared to multispectral ones. To achieve accurate land cover classification over a large coverage, we propose a cross-modality feature learning framework, called common subspace learning (CoSpace), by jointly considering subspace learning and supervised classification. By locally aligning the manifold structure of the two modalities, CoSpace linearly learns a shared latent subspace from hyperspectral-multispectral(HS-MS) correspondences. The multispectral out-of-samples can be then projected into the subspace, which are expected to take advantages of rich spectral information of the corresponding hyperspectral data used for learning, and thus leads to a better classification. Extensive experiments on two simulated HSMS datasets (University of Houston and Chikusei), where HS-MS data sets have trade-offs between coverage and spectral resolution, are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.

• The paper introduces a cross-modality feature learning framework that integrates hyperspectral and multispectral data via a shared latent subspace to enhance land cover classification.
• It employs an ADMM-based optimization algorithm to align manifold structures of HS and MS datasets, yielding superior classification accuracy over state-of-the-art methods.
• Experimental validation on the University of Houston and Chikusei datasets demonstrates practical benefits for remote sensing applications in urban monitoring and ecological management.

Overview of CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences

The paper "CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences" introduces a framework for improving land cover classification through the integration of hyperspectral (HS) and multispectral (MS) data. This research confronts the inherent challenges posed by the trade-off between the broad coverage of MS imaging and the rich spectral detail provided by HS imaging. The proposed framework, CoSpace, addresses these challenges by deploying a cross-modality feature learning strategy that leverages HS-MS correspondences to learn a shared latent subspace for enhanced classification.

Key Contributions

1. Cross-Modality Feature Learning Framework: CoSpace innovatively blends subspace learning with supervised classification to bridge the gap between the HS and MS data modalities. This is achieved through a shared latent subspace learned from HS-MS correspondences, effectively transferring the superior spectral detail from HS data to enhance MS data classification.
2. Model Formulation and Optimization: The authors formulated the CoSpace model to effectively align the manifold structures of HS and MS datasets within a common subspace, facilitating improved classification performance. The paper details an optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve the associated optimization problem efficiently.
3. Assessment through Extensive Experiments: The efficacy of CoSpace was rigorously evaluated using two simulated HS-MS datasets—specifically, those from the University of Houston and Chikusei. These experiments highlighted CoSpace's superiority over other state-of-the-art methods, demonstrating significant improvements in classification accuracy through empirical evaluations using multiple classifiers such as the nearest neighbor (NN), linear support vector machines (LSVM), and canonical correlation forest (CCF).

Implications and Future Directions

The implications of this research are twofold: practical and theoretical. Practically, CoSpace offers a robust methodology for enhancing land cover classification accuracy, a critical task in urban monitoring, ecological management, and disaster prediction using remote sensing imagery. Theoretically, this framework advances the understanding of common subspace learning, particularly in cross-modal remote sensing data integration.

Future work may explore further the extension of CoSpace to heterogeneous data sources, explicitly addressing its application under varied cross-domain scenarios beyond optical imagery. Specifically, incorporating deep learning frameworks could address potential nonlinearities not captured by the current linear model.

Additionally, expanding CoSpace to accommodate varying classes across large-scale MS imagery, a more complex yet realistic scenario, represents another avenue for subsequent research. This would necessitate an adaptive model capable of handling an increased diversity of land-cover types.

Conclusion

This work stands as a valuable contribution to geospatial data analysis, bridging the gap in hyperspectral and multispectral imaging through a succinct and robust learning framework. CoSpace's potential to enhance land cover classification underscores its significance as a tool for future remote sensing applications and research advancements.