Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification
Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification
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DOI：
10.1016/j.isprsjprs.2018.10.006
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发表时间：
2019-01-01
影响因子：
12.7
通讯作者：
Zhu, Xiao Xiang
中科院分区：
文献类型：
--
作者：
Hong, Danfeng;Yokoya, Naoto;Zhu, Xiao Xiang
在本文中，我们的目标是解决遥感领域中一个普遍但有趣的跨模态特征学习问题-可以使用有限数量的高分辨（例如，高光谱）训练数据使用大量的差辨别性（例如，多光谱）数据？传统的半监督流形对齐方法对于这样的问题表现得不够好，因为与多光谱数据相比，高光谱数据在时间和效率之间的权衡中被大量收集是非常昂贵的。为此，我们提出了一种新的半监督跨模态学习框架，称为可学习流形对齐（LeMA）。LeMA直接从数据中学习联合图结构，而不是使用由高斯核函数定义的给定固定图。通过学习的图，我们可以通过基于图的标签传播进一步捕获数据分布，从而找到更准确的决策边界。此外，基于交替方向乘子法（ADMM）的优化策略的设计，以解决所提出的模型。在两个高光谱-多光谱数据集上的实验结果表明了该方法与现有方法相比的优越性和有效性。
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-discriminative (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.