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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Exploring Locally Adaptive Dimensionality Reduction for Hyperspectral Image Classification: A Maximum Margin Metric Learning Aspect
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Exploring Locally Adaptive Dimensionality Reduction for Hyperspectral Image Classification: A Maximum Margin Metric Learning Aspect

机译：探索用于高光谱图像分类的局部自适应降维：最大幅度度量学习方面

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摘要

The high-dimensional data space generated by hyperspectral sensors introduces challenges for the conventional data analysis techniques. Popular dimensionality reduction techniques usually assume a Gaussian distribution, which may not be in accordance with real life. Metric learning methods, which explore the global data structure of the labeled training samples, have proved to be very efficient in hyperspectral fields. However, we can go further by utilizing locally adaptive decision constraints for the labeled training samples per class to obtain an even better performance. In this paper, we present the locally adaptive dimensionality reduction metric learning (LADRml) method for hyperspectral image classification. The aims of the presented method are: 1) first, to utilize the limited training samples to reduce the dimensionality of data without a certain distribution hypothesis; and 2) second, to better handle data with complex distributions by the use of locally adaptive decision constraints, which can assess the similarity between a pair of samples based on the distance changes before and after metric learning. The experimental results obtained with a number of challenging hyperspectral image datasets demonstrate that the proposed LADRml algorithm outperforms the state-of-the-art dimensionality reduction and metric learning methods.
机译：高光谱传感器生成的高维数据空间为常规数据分析技术带来了挑战。流行的降维技术通常采用高斯分布，这可能与现实生活不符。在高光谱领域，探索学习标签训练样本的全局数据结构的度量学习方法已被证明是非常有效的。但是，我们可以通过对每个班级的标记训练样本使用本地自适应决策约束来获得更好的性能，从而走得更远。在本文中，我们提出了用于高光谱图像分类的局部自适应降维度量学习（LADRml）方法。该方法的目的是：1）首先，利用有限的训练样本来减少数据的维数，而没有一定的分布假设。 2）其次，通过使用局部自适应决策约束更好地处理具有复杂分布的数据，该决策约束可以基于度量学习前后的距离变化来评估一对样本之间的相似性。使用大量具有挑战性的高光谱图像数据集获得的实验结果表明，提出的LADRml算法优于最新的降维和度量学习方法。

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