基于深度学习的高光谱遥感图像混合像元分解研究综述

苏远超; 许若晴; 高连如; 韩竹; 孙旭

doi:10.11834/jrs.20243165

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专辑

基于深度学习的高光谱遥感图像混合像元分解研究综述
Development of deep learning-based hyperspectral remote sensing image unmixing
2024年28卷第1期页码：1-19
纸质出版日期： 2024-01-07 ，
DOI： 10.11834/jrs.20243165
稿件说明：

移动端阅览

苏远超，许若晴，高连如，韩竹，孙旭.2024.基于深度学习的高光谱遥感图像混合像元分解研究综述.遥感学报，28（1）： 1-19

Su Y C，Xu R Q，Gao L R，Han Z and Sun X. 2024. Development of deep learning-based hyperspectral remote sensing image unmixing. National Remote Sensing Bulletin， 28（1）：1-19
苏远超，许若晴，高连如，韩竹，孙旭.2024.基于深度学习的高光谱遥感图像混合像元分解研究综述.遥感学报，28（1）： 1-19 DOI： 10.11834/jrs.20243165.

Su Y C，Xu R Q，Gao L R，Han Z and Sun X. 2024. Development of deep learning-based hyperspectral remote sensing image unmixing. National Remote Sensing Bulletin， 28（1）：1-19 DOI： 10.11834/jrs.20243165.

摘要

高光谱遥感是以成像光谱学为基础发展起来的一项综合性遥感技术，它能够同步记录成像区域内地物的空间信息和光谱信号，故而也称为“成像光谱遥感”。高光谱遥感所获取的数据称为“高光谱遥感图像”，相较于传统的遥感数据，高光谱遥感图像具有光谱分辨率高和“图谱合一”的特点，目前已成为遥感工程应用中的重要支撑数据之一。然而，受空间分辨率限制，混合像元（即某一像元内包含多种类型的地物）问题始终限制着高光谱遥感在精细化地物信息提取工作中的作用。混合像元分解（“解混”）是现阶段处理混合像元问题最有效的分析方法，旨在从亚像元角度出发，获取像元中纯净的光谱信号（“端元”），并分析出各类端元在像元内所占的比例（“丰度”）。在遥感领域，为实现地物信息精细化解译，目前已发展出不同类型的解混方法，在一定程度上解决了混合像元问题对遥感定量化分析的制约。如今，随着深度学习的发展，越来越多的先进理论和工具被用于处理混合像元问题，发展出了一类基于深度学习的新型解混方法。这些新方法以光谱混合模型为桥梁，用深度学习方式来解译光谱混合现象。相比于传统的解混方法，基于深度学习的解混方法在隐藏信息的挖掘和利用方面更具优势，对先验知识依赖程度相对较低，对复杂场景的适应性更强。近年来，基于深度学习的解混方法发展迅速，并且在植被分布调查、农业产量估算等经常涉及混合像元问题的工作中被逐渐普及，有很好的发展前景和应用价值。本文以光谱混合模型和训练方式为基础，对现阶段基于深度学习解混的研究成果进行归类，并从不同类别的特点出发，对现有基于深度学习的解混方法进行介绍。最后，对当前的技术状况、特点和发展前景进行总结与展望，为今后解混技术的研究与应用提供参考。

Abstract

Hyperspectral remote sensing is an advanced technique for earth observation that combines physical imagery and spectral analysis technology. Therefore

hyperspectral remote sensing can obtain fine spectral and rich spatial information from imaged scenes

merging the spatial and spectral information into data cubes. These data cubes exhibit narrow spectral bands and a high spectral resolution

allowing different land cover objects to be distinguished. Hyperspectral remote sensing images

with their high spectral resolution and cube characteristics

have gradually become among the most essential supporting data in remote sensing engineering applications. However

due to spatial resolution limitations

the mixed pixel problem has hindered the development of hyperspectral remote sensing in fine-scale object information extraction. At present

hyperspectral unmixing is one of the most effective analytical techniques for dealing with mixed pixel problems

aiming to break through spatial resolution limitations by analyzing the components within pixels. Hyperspectral unmixing refers to any process that separates pixel spectra from a hyperspectral image into a collection of pure constituent spectra

called endmembers

and a set of corresponding abundance fractions. At each pixel

the endmembers are generally assumed to represent the pure materials in the scene

while the abundances represent the percentage of each endmember. For the fine-scale interpretation of object information

many unmixing methods have been developed for hyperspectral remote sensing images in the remote sensing field over the past 30 years

mitigating the impact of mixed pixel problems on quantitative remote sensing analysis. Currently

with the development of deep learning

an increasing number of deep learning theories and tools are used to deal with mixed pixel problems. Many new methods using deep learning for unmixing have been developed

and unmixing technology research has gradually entered a new stage of development with deep learning. Deep-learning-based methods make better use of hidden information

have a relatively lower dependence on prior knowledge

and have a stronger adaptability to complex scenes than traditional unmixing methods. Although deep learning-based unmixing methods have developed rapidly in recent years and are diverse

the analysis and summary of the work on such methods have not kept up with the pace of technological development. A timely summary of the latest research progress on developing a specific field of research has a significant role in promoting the technology. Thus

this paper sorts out the existing deep learning-based unmixing methods

classifying them according to the adopted spectral mixing models

the deep network training modes

and whether spectral variability is considered. Furthermore

this paper introduces these deep learning-based approaches and summarizes their characteristics

making the use of these methods in special works convenient for users or readers. Finally

the development of deep learning methods is summarized

referring to the current technical status

characteristics

and development prospects. In addition

some existing deep learning unmixing methods were tested in this study and organized to facilitate the research and application of unmixing technology. The development of deep learning will continue to promote the progress of unmixing techniques. In recent years

deep learning-based unmixing methods have developed rapidly and have been gradually used in vegetation distribution investigation and agricultural yield estimation

implying their good development prospect and application value. his paper can provide valuable references for researching unmixing technology in the future.

关键词

高光谱遥感混合像元分解深度学习机器学习深度神经网络遥感图像处理遥感智能解译亚像元解译

Keywords

hyperspectral remote sensingunmixingdeep learningmachine learningdeep neural networkremote sensing image processingremote sensing intelligent interpretationsubpixel interpretation

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