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Dimensionality reduction for hyperspectral remote sensing： Advances， challenges， and prospects

  • role： First author第一作者
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

  • Email：hjsu@hhu.edu.cn
  • Introduction：苏红军，研究方向为高光谱遥感、资源环境遥感等。E-mail： hjsu@hhu.edu.cn
SU Hongjun

resumen

Hyperspectral imaging can provide narrow bands and continuous spectrum information. However, hyperspectral image data have the characteristics of high dimensionality, rich features, information redundancy, small samples, and significant uncertainty, which result in difficulties in hyperspectral image data processing. Dimensionality reduction of hyperspectral remote sensing is one of the important topics in hyperspectral image data processing. Hyperspectral image data have hundreds of bands and can provide rich information, but a strong correlation exists between different bands, resulting in data redundancy. Therefore, the dimensionality problem is encountered during the processing of hyperspectral data, such as the increase in time complexity and the overfitting of the prediction model due to the increase in spectral feature dimension. More importantly, the number of training samples available for hyperspectral remote sensing images is small, and the feature dimension is much larger than the training sample. The classification accuracy will increase first and then decrease with the increase of feature dimensionality, that is, the “Hughes” phenomenon. Therefore, exploiting the rich information of hyperspectral images data and solving the problem of high feature dimension through certain methods have become key issues in the research on hyperspectral imaging data processing. The dimensionality reduction of hyperspectral remote sensing image is an approach to reduce the dimensionality of hyperspectral imaging through feature extraction or band selection while retaining as much effective information or features as possible. Feature extraction methods, such as principal component analysis, linear discriminant analysis, independent component analysis, manifold learning, and deep learning-based methods, use the projection transformation method to map hyperspectral data from high-dimensional space to low-dimensional space. Feature selection eliminates redundant bands without changing the original feature structure and finds representative feature band subsets, such as the selection based on information measurement and feature correlation. With the development of new technologies, evolutionary and intelligent algorithms, such as the genetic, ant colony, and firefly algorithms, have been applied in hyperspectral remote sensing dimensionality reduction.This article systematically summarizes and reviews the current advances in dimensionality reduction for hyperspectral remote sensing, especially for feature extraction and selection. For feature extraction, we review the advances of feature extraction algorithms based on index and parameters, projection and transformation, band combination, spatial algorithm, manifold learning, and deep learning. For band selection, the advances in information measurement, search strategy, optimized band number, multi-feature quality assessment, and optimization algorithms are reviewed. The challenges of dimensionality reduction for hyperspectral remote sensing are analyzed from five aspects: feature separability, feature quality evaluation, feature number determination, multi-feature optimization, and problem-oriented feature selection. Intelligent dimensionality reduction will be one of the most popular topics with the development of intelligent hyperspectral remote sensing. Meanwhile, multi-feature quality assessment, search strategy optimization and application requirements will attract special attention in the future. The dimensionality reduction of hyperspectral remote sensing will play an important and irreplaceable role in hyperspectral image data acquisition and applications.

palabra clave

hyperspectral remote sensing;dimensionality reduction;feature extraction;feature selection;multiple features optimization

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