高光谱遥感影像降维：进展、挑战与展望

苏红军

doi:10.11834/jrs.20210354

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高光谱遥感影像降维：进展、挑战与展望
Dimensionality reduction for hyperspectral remote sensing： Advances， challenges， and prospects
2022年26卷第8期页码：1504-1529
纸质出版日期： 2022-08-07 ，
DOI： 10.11834/jrs.20210354

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苏红军.2022.高光谱遥感影像降维：进展、挑战与展望.遥感学报，26（8）： 1504-1529

Su H J. 2022. Dimensionality reduction for hyperspectral remote sensing： Advances， challenges， and prospects. National Remote Sensing Bulletin， 26（8）：1504-1529
苏红军.2022.高光谱遥感影像降维：进展、挑战与展望.遥感学报，26（8）： 1504-1529 DOI： 10.11834/jrs.20210354.

Su H J. 2022. Dimensionality reduction for hyperspectral remote sensing： Advances， challenges， and prospects. National Remote Sensing Bulletin， 26（8）：1504-1529 DOI： 10.11834/jrs.20210354.

摘要

高光谱遥感影像数据具有高维特征、信息冗余、不确定性显著、小样本、空谱合一等特征，对其进行数据处理面临巨大挑战，高光谱遥感影像降维是高光谱遥感的重要研究方向之一。本文对当前高光谱遥感影像降维的相关研究进展进行了综述，在介绍高光谱遥感数据特点的基础上，重点从特征提取和特征选择两方面对高光谱遥感影像降维的最新研究和前沿进展进行了系统性综述；并从特征可分性、特征质量评价、特征数目确定、多特征优化以及需求驱动的特征选择等方面分析了高光谱遥感影像降维面临的挑战。随着智能化高光谱遥感的发展，高光谱遥感影像智能降维成为未来的发展方向，同时其发展将兼顾多特征质量评估与优选、搜索策略优化、满足应用需求等多目标的需求。随着高光谱遥感数据获取能力的提升和深入应用，高光谱遥感影像降维将会发挥重要而不可替代的作用。

Abstract

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.

关键词

高光谱遥感数据降维特征提取特征选择多特征优化

Keywords

hyperspectral remote sensingdimensionality reductionfeature extractionfeature selectionmultiple features optimization

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