# Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification

arXiv: https://arxiv.org/abs/1901.02838 · HTML: https://mlsanity.com/paper/1901.02838/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2019.

## Built on by (most cited first, 17 of 17)

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model](https://mlsanity.com/paper/2105.10196/) (2021)
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- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery](https://mlsanity.com/paper/2106.00880/) (2021)
- [Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2201.01001/) (2022)
- [Distributed Learning over Networks with Graph-Attention-Based Personalization](https://mlsanity.com/paper/2305.13041/) (2023)
- [Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data](https://mlsanity.com/paper/2210.17311/) (2022)
- [Cross-Modal and Multimodal Data Analysis Based on Functional Mapping of Spectral Descriptors and Manifold Regularization](https://mlsanity.com/paper/2105.05631/) (2021)
- [Triplet-Watershed for Hyperspectral Image Classification](https://mlsanity.com/paper/2103.09384/) (2021)
- [Weakly Supervised Instance Attention for Multisource Fine-Grained Object Recognition with an Application to Tree Species Classification](https://mlsanity.com/paper/2105.10983/) (2021)
- [A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection](https://mlsanity.com/paper/2112.04388/) (2021)
- [Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing](https://mlsanity.com/paper/2103.01449/) (2021)
- [Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification](https://mlsanity.com/paper/2208.02613/) (2022)
- [Changes from Classical Statistics to Modern Statistics and Data Science](https://mlsanity.com/paper/2211.03756/) (2022)

## Abstract (arXiv)

In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community --- can a limited amount of highly-discrimin-ative (e.g., hyperspectral) training data improve the performance of a classification task using a large amount of poorly-discriminative (e.g., multispectral) data? Traditional semi-supervised manifold alignment methods do not perform sufficiently well for such problems, since the hyperspectral data is very expensive to be largely collected in a trade-off between time and efficiency, compared to the multispectral data. To this end, we propose a novel semi-supervised cross-modality learning framework, called learnable manifold alignment (LeMA). LeMA learns a joint graph structure directly from the data instead of using a given fixed graph defined by a Gaussian kernel function. With the learned graph, we can further capture the data distribution by graph-based label propagation, which enables finding a more accurate decision boundary. Additionally, an optimization strategy based on the alternating direction method of multipliers (ADMM) is designed to solve the proposed model. Extensive experiments on two hyperspectral-multispectral datasets demonstrate the superiority and effectiveness of the proposed method in comparison with several state-of-the-art methods.