高光谱遥感图像本征信息分解前沿与挑战

doi:10.11947/j.AGCS.2023.20220563

测绘学报 ›› 2023, Vol. 52 ›› Issue (7): 1059-1073.doi: 10.11947/j.AGCS.2023.20220563

高光谱遥感图像本征信息分解前沿与挑战

李树涛¹, 吴琼¹, 康旭东²

1. 湖南大学电气与信息工程学院, 湖南长沙 410082;
2. 湖南大学机器人学院, 湖南长沙 410012

收稿日期:2022-09-30 修回日期:2023-06-20 发布日期:2023-07-31
通讯作者: 康旭东 E-mail:xudong_kang@163.com
作者简介:李树涛(1972-),男,博士,教授,研究方向为遥感图像处理、信息融合与模式识别。E-mail:shutao_li@hnu.edu.cn
基金资助:
国家重点研发计划（2021YFA0715203）；国家自然科学基金（62221002；61890962；61871179；62201207）；湖南省国家科学基金（2020GK2038）；湖南省自然科学基金杰出青年（2021JJ022）；湖湘青年人才科技创新计划（2020RC3013）；中国博士后科学基金（2022M721106）

Hyperspectral remote sensing image intrinsic information decomposition: advances and challenges

LI Shutao¹, WU Qiong¹, KANG Xudong²

1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;
2. School of Robotics, Hunan University, Changsha 410012, China

Received:2022-09-30 Revised:2023-06-20 Published:2023-07-31
Supported by:
The National Key Research and Development Program of China (No. 2021YFA0715203);The National Natural Science Foundation of China (Nos. 62221002; 61890962; 61871179; 62201207); The National Science Foundation of Hunan Province (No. 2020GK2038); The Hunan Provincial Natural Science Foundation for Distinguished Young Scholars (No. 2021JJ022); The Huxiang Young Talents Science and Technology Innovation Program (No. 2020RC3013); The Fellowship of China Postdoctoral Science Foundation (No. 2022M721106)

摘要/Abstract

摘要： 高光谱作为一种图谱合一的成像技术，在对地观测、航空航天领域具有十分重要的应用。然而，作为光学遥感的分支，高光谱成像易受到大气、光照等因素的影响。高光谱图像本征信息分解旨在抑制复杂环境因素对地物光谱与空间特征的影响，准确提取并表征观测场景最本征的光谱与空间信息，提升高光谱图像识别与解译性能。本文主要对代表性的高光谱图像本征信息分解的模型和方法进行综述，系统地分析了各种典型方法的原理及优缺点，进一步阐述了实际遥感应用中现有本征信息分解面临的挑战性难题，并结合遥感实际应用，对高光谱图像本征信息分解技术的发展趋势进行了展望。

关键词: 高光谱遥感, 人工智能, 本征信息分解, 图像识别与解译

Abstract: Hyperspectral imaging is a powerful image acquisition method which can record the rich spectral and spatial information of the scene in a high dimensional data cube. Due to this advantage, hyperspectral imaging has been very useful in many practical applications of earth observation and aerospace. However, as a branch of optical remote sensing, the performance of hyperspectral imaging may be affected by many factors such as atmosphere and illumination. The objective of hyperspectral intrinsic image decomposition is to decrease the influence of complex environmental factors, extract and represent the intrinsic spectral and spatial information of hyperspectral images accurately, so as to improve the performance of hyperspectral image recognition and interpretation. This paper reviews some representative work in hyperspectral intrinsic image decomposition. The principle, advantages, and disadvantages of some typical intrinsic image decomposition methods have been analyzed. Moreover, the challenging problems of intrinsic image decomposition faced in real remote sensing applications have been illustrated. At last, based on the requirements of practical remote sensing applications, we discuss the development trends of hyperspectral intrinsic image decomposition. This review could be a good guide for those researchers who are interested in the advances and applications of hyperspectral remote sensing. More importantly, it gives some important future research directions that could be investigated in the future.

Key words: hyperspectral remote sensing, artificial intelligence, intrinsic image decomposition, image recognition and interpretation

中图分类号:

P237

李树涛, 吴琼, 康旭东. 高光谱遥感图像本征信息分解前沿与挑战[J]. 测绘学报, 2023, 52(7): 1059-1073.

LI Shutao, WU Qiong, KANG Xudong. Hyperspectral remote sensing image intrinsic information decomposition: advances and challenges[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(7): 1059-1073.

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高光谱遥感图像本征信息分解前沿与挑战

Hyperspectral remote sensing image intrinsic information decomposition: advances and challenges

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