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Dimension Reduction Aided Hyperspectral Image Classification with a Small-sized Training Dataset: Experimental Comparisons

机译：小型训练数据集的降维辅助高光谱图像分类：实验比较

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摘要

Hyperspectral images (HSI) provide rich information which may not be captured by other sensing technologies and therefore gradually find a wide range of applications. However, they also generate a large amount of irrelevant or redundant data for a specific task. This causes a number of issues including significantly increased computation time, complexity and scale of prediction models mapping the data to semantics (e.g., classification), and the need of a large amount of labelled data for training. Particularly, it is generally difficult and expensive for experts to acquire sufficient training samples in many applications. This paper addresses these issues by exploring a number of classical dimension reduction algorithms in machine learning communities for HSI classification. To reduce the size of training dataset, feature selection (e.g., mutual information, minimal redundancy maximal relevance) and feature extraction (e.g., Principal Component Analysis (PCA), Kernel PCA) are adopted to augment a baseline classification method, Support Vector Machine (SVM). The proposed algorithms are evaluated using a real HSI dataset. It is shown that PCA yields the most promising performance in reducing the number of features or spectral bands. It is observed that while significantly reducing the computational complexity, the proposed method can achieve better classification results over the classic SVM on a small training dataset, which makes it suitable for real-time applications or when only limited training data are available. Furthermore, it can also achieve performances similar to the classic SVM on large datasets but with much less computing time.
机译：高光谱图像（HSI）提供了其他传感技术无法捕获的丰富信息，因此逐渐找到了广泛的应用范围。但是，它们还会为特定任务生成大量不相关或冗余的数据。这导致许多问题，包括显着增加的计算时间，将数据映射到语义的预测模型的复杂性和规模（例如，分类），以及需要大量标记数据进行训练。特别是，在许多应用中，专家通常难以获得足够的训练样本，而且费用昂贵。本文通过探索机器学习社区中用于HSI分类的许多经典降维算法来解决这些问题。为了减少训练数据集的大小，采用了特征选择（例如，互信息，最小冗余最大相关性）和特征提取（例如，主成分分析（PCA），内核PCA）来增强基线分类方法，支持向量机（ SVM）。使用真实的HSI数据集对提出的算法进行评估。结果表明，PCA在减少特征或光谱带的数量方面具有最有前途的性能。可以看出，在显着降低计算复杂度的同时，所提出的方法可以在较小的训练数据集上获得优于传统SVM的更好分类结果，这使其适合于实时应用或仅可获得有限的训练数据。此外，它还可以在大型数据集上实现与传统SVM相似的性能，但计算时间却少得多。

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