Abstract. A large amount of remote sensing data can be easily acquired due to the increase in the advances in sensor’s technologies. The sensors can generate high-dimensional data in a lower time producing problems related to big data such as management and organization. Since the acquired data is characterized by a large dimension and lack of structure, the information analysis becomes harder. Therefore, an organization stage should structure the data reducing the dimension while maintaining the main properties to enable further analysis. The feature extraction and selection methods can achieve this task. Consequently, we aim to explore various pixel-wise feature extraction and selection algorithms to manage the organization stage of big data for hyperspectral images. Our work covers the comparison between feature vectors computed using the discrete Fourier transform, discrete cosine transform (DCT), and stationary wavelet transform. Moreover, spectral angle mapper, Jeffries–Matusita distance, spectral information divergence, and linear discriminant analysis (LDA) were implemented as feature selectors. Feature extraction and selection methods were combined and evaluated in terms of algorithm complexity, reduction efficiency, and classification accuracy with the aid of a support vector machine and a maximum likelihood classifier. The analysis shows that some linear transformations can perform better in natural landscapes and others in urban images. Furthermore, the study found that the combination of DCT and LDA, which achieves high classification rates with an efficient dimension reduction, can be suitable for the organization stage of a big data remote sensing application of hyperspectral images.

中文翻译：

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用于大数据遥感应用的高光谱图像的有效降维

摘要。由于传感器技术的进步，可以很容易地获取大量的遥感数据。传感器可以在更短的时间内生成高维数据，从而产生与管理和组织等大数据相关的问题。由于获取的数据具有维数大、结构欠缺的特点，信息分析变得更加困难。因此，组织阶段应该在保持主要属性的同时减少维度的数据结构化，以便进行进一步的分析。特征提取和选择方法可以完成这个任务。因此，我们旨在探索各种像素级特征提取和选择算法，以管理高光谱图像大数据的组织阶段。我们的工作涵盖了使用离散傅立叶变换、离散余弦变换 (DCT) 和平稳小波变换计算的特征向量之间的比较。此外，光谱角度映射器、Jeffries-Matusita 距离、光谱信息发散和线性判别分析 (LDA) 被实现为特征选择器。在支持向量机和最大似然分类器的帮助下，将特征提取和选择方法结合起来，并在算法复杂度、约简效率和分类精度方面进行评估。分析表明，一些线性变换在自然景观中表现更好，而其他在城市图像中表现更好。此外，研究发现，DCT 和 LDA 的组合，在有效降维的情况下实现了高分类率，