高光谱图像分数域信息提取理论与方法进展

赵旭东; 陶然; 李伟; 张蒙蒙

doi:10.12263/DZXB.20221215

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高光谱图像分数域信息提取理论与方法进展

《电子学报》创刊60周年专栏 | 更新时间：2025-12-11

- 高光谱图像分数域信息提取理论与方法进展
- Recent Developments in Fractional Information Extraction Theory and Methods of Hyperspectral Image
- 电子学报 2022年50卷第12期页码：2874-2883
- 作者机构：
  
  北京理工大学信息与电子学院，北京 100081
- 作者简介：
  
  [ "赵旭东男，1993年10月出生于河北省张家口市.北京理工大学信息与电子学院博士研究生.主要研究方向为分数域信号处理.E-mail: xudongzhao461@gmail.com" ]
  [ "陶然男，1964年11月出生于安徽省芜湖市.现为北京理工大学信息与电子学院教授.主要研究方向为分数域信号与信息处理及其应用." ]
  [ "李伟男，1985年2月出生于湖北省随州市.现为北京理工大学信息与电子学院教授.主要研究方向为高光谱图像处理.E-mail: liw@bit.edu.cn" ]
  [ "张蒙蒙女，1994年6月出生于山东省济宁市.现为北京理工大学信息与电子学院副研究员.主要研究方向为高光谱图像处理.E-mail: mengmengzhang@bit.edu.cn" ]
- 基金信息：
  
  北京市自然科学基金(JQ20021)
- DOI：10.12263/DZXB.20221215
  中图分类号： TN911.73;
- 收稿：2022-10-26，
  
  修回：2023-01-02，
  
  纸质出版：2022-12-25
- 稿件说明：
移动端阅览
赵旭东,陶然,李伟等.高光谱图像分数域信息提取理论与方法进展[J].电子学报,2022,50(12):2874-2883.

ZHAO Xu-dong,TAO Ran,LI Wei,et al.Recent Developments in Fractional Information Extraction Theory and Methods of Hyperspectral Image[J].ACTA ELECTRONICA SINICA,2022,50(12):2874-2883.
赵旭东,陶然,李伟等.高光谱图像分数域信息提取理论与方法进展[J].电子学报,2022,50(12):2874-2883. DOI： 10.12263/DZXB.20221215.

ZHAO Xu-dong,TAO Ran,LI Wei,et al.Recent Developments in Fractional Information Extraction Theory and Methods of Hyperspectral Image[J].ACTA ELECTRONICA SINICA,2022,50(12):2874-2883. DOI： 10.12263/DZXB.20221215.

摘要

高光谱传感技术具有光谱、空间、辐射等信息同步获取的优势，促使地物描述呈现多尺度、多角度、多维度的特性，基于高光谱图像实施信息精确提取及解析，是建设先进对地观测系统等国家战略的核心需求.然而，高光谱图像信息提取存在光谱维诊断信息不确定、空间维信息利用不充分、协同维信息表征不全面等问题，导致信息感知与解译效果差，严重制约了其行业应用.高光谱智能感知在对地观测等多元领域中的应用，需要机理清晰、关联合理、协同有效的多域信息提取理论与方法.本文综述了高光谱图像分数域信息提取理论与方法进展，首先给出了现有的高光谱图像信息提取方法及其关键问题，然后从光谱维、空谱维、协同维三方面给出了高光谱图像分数域信息提取理论与方法体系.本文随后从光谱维精确控制、空谱维强化感知、协同维信息联合三方面分别总结了关键理论与应用：在光谱维，针对高光谱图像光谱信息提取易受到光谱不确定性现象影响而难以准确区分微弱点目标与复杂背景的问题，分数域光谱信号表征方法可提升光谱域辨识性能，实施高光谱异常检测；在空谱维，针对高光谱场景中地物空间分布不均衡，标签样本不足导致场景解译困难的问题，分数域空谱特征提取方法在有效训练集合扩充的同时提升网络学习的多样性，实施小样本情况下的场景解译；在协同维，针对高光谱与其他传感源存在异质性，导致地物三维信息表征不全面的问题，分数域多源协同特征提取与融合方法可实现多源多域特征联合，完成高精度地物分类.最后，本文指出了未来高光谱图像分数域信息提取理论与方法面临的挑战和发展趋势：面向高光谱数据体量大、分辨率较差等局限性，开展数据、特征层结合的质量提升方法研究；面向训练样本缺失问题，通过深度特征迁移学习技术，充分挖掘高光谱遥感图像中海量无标签数据的多维度光谱、空间、协同信息；面向广域空天遥感对地观测需求，研究模态缺失情况下的深度样本生成、特征扩充、多源跨场景分类方法.

Abstract

Hyperspectral sensing technology can acquire spectral

spatial

radiation and other information synchronously

which provides the multi-scale

multi-angle

and multi-dimensional features of land covers. However

there are significant challenges in hyperspectral information extraction

e.g.

spectral uncertainty

insufficient utilization of spatial information

and incomplete representation of collaborative information

resulting in poor information extraction and scene interpretation. The applications of hyperspectral interpretation

e.g.

earth observation

requires multi-domain information extraction theories and methods to breakthrough these problems. In this survey

we firstly present the existing methods for hyperspectral information extraction and their main problems

and then introduce the fractional information extraction theory and methods of hyperspectral image

which consists of spectral dimension

spatial-spectral dimension

and collaborative dimension. Then

the main theories and applications are introduced

including spectral information adjustment

spatial-spectral information enhancement

and information fusion and transferring of multisource remote sensing data. For spectral dimension

the spectral uncertainty phenomenon makes it difficult to distinguish small targets from complex backgrounds. Focusing on this problem

the fractional-domain spectral information extraction method can improve the performance of hyperspectral anomaly detection. For spatial dimension

the complex spatial distribution of hyperspectral scenes and the lack of labeled samples make the scene interpretation challenging. Focusing on this problem

the fractional-domain spatial-spectral feature extraction methods can effectively generate more discriminative training features and improve the diversity of training sets

which contribute to handling small sample size problems. For the collaborative dimension

the fractional-domain multi-source feature extraction and fusion method can realize the joint use of multi-source and multi-domain features

and achieve high-precision classification. Finally

this survey points out the challenges and development trends of fractional information extraction theory for hyperspectral images. To breakthrough the limitations of hyperspectral data

e.g.

high-dimension and low-resolution

it is important to improve the data quality at data- and feature-level. To solve the problem of unavailable training samples

transfer learning techniques are in need to fully exploit the spectral

spatial and collaborative information of the massive unlabeled data in hyperspectral remote sensing images. Targeting the global-scale earth observation by remote sensing

focusing on the condition when some modalities are missing

researches on domain generation and cross-scene classification are in need.

关键词

Keywords

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