Recent Developments in Fractional Information Extraction Theory and Methods of Hyperspectral Image

ZHAO Xu-dong; TAO Ran; LI Wei; ZHANG Meng-meng

doi:10.12263/DZXB.20221215

您当前的位置：

首页 >

文章列表页 >

Recent Developments in Fractional Information Extraction Theory and Methods of Hyperspectral Image

60th Anniversary Special Issue | 更新时间：2025-12-11

- Recent Developments in Fractional Information Extraction Theory and Methods of Hyperspectral Image
- ACTA ELECTRONICA SINICA Vol. 50, Issue 12, Pages: 2874-2883(2022)
- 作者机构：
  
  北京理工大学信息与电子学院，北京 100081
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20221215
  CLC： TN911.73;
- Received：26 October 2022，
  
  Revised：2023-01-02，
  
  Published：25 December 2022
- 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

references

LANDGREBE D . Hyperspectral image data analysis [J ] . IEEE Signal Processing Magazine , 2002 , 19 ( 1 ): 17 - 28 .

童庆禧 , 张兵 , 郑兰芬 . 高光谱遥感: 原理、技术与应用 [M ] . 北京 : 高等教育出版社 , 2006 .

TONG Q X , ZHANG B , ZHENG L F . Hyperspectral Remote Sensing [M ] . Beijing : Higher Education Press , 2006 . (in Chinese)

张兵 , 高连如 . 高光谱图像分类与目标探测 [M ] . 北京 : 科学出版社 , 2011 : 85 - 101 .

ZHANG B , GAO L R . Hyperspectral Image Classification and Target Detection [M ] . Beijing : Science Press , 2011 : 85 - 101 . (in Chinese)

CHEN C , LI W , SU H J , et al . Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine [J ] . Remote Sensing , 2014 , 6 ( 6 ): 5795 - 5814 .

DALPONTE M , BRUZZONE L , GIANELLE D . Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2008 , 46 ( 5 ): 1416 - 1427 .

XU X D , LI W , RAN Q , et al . Multisource remote sensing data classification based on convolutional neural network [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 2 ): 937 - 949 .

RAVIKANTH L , JAYAS D S , WHITE N D G , et al . Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products [J ] . Food and Bioprocess Technology , 2017 , 10 ( 1 ): 1 - 33 .

CAMPS-VALLS G , TUIA D , BRUZZONE L , et al . Advances in hyperspectral image classification: Earth monitoring with statistical learning methods [J ] . IEEE Signal Processing Magazine , 2014 , 31 ( 1 ): 45 - 54 .

DURKEE N , ASH J N , MEOLA J . Longwave infrared hyperspectral emissivity retrieval with robustness to spectral uncertainty [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 13 .

TAO C , PAN H B , LI Y S , et al . Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification [J ] . IEEE Geoscience and Remote Sensing Letters , 2015 , 12 ( 12 ): 2438 - 2442 .

CHEN C , LI W , SU H J , et al . Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine [J ] . Remote Sensing , 2014 , 6 ( 6 ): 5795 - 5814 .

MEI X G , PAN E T , MA Y , et al . Spectral-spatial attention networks for hyperspectral image classification [J ] . Remote Sensing , 2019 , 11 ( 8 ): 963 .

HANG R L , LI Z , GHAMISI P , et al . Classification of hyperspectral and LiDAR data using coupled CNNs [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2020 , 58 ( 7 ): 4939 - 4950 .

EHLERS M , KLONUS S , JOHAN ÅSTRAND P , et al . Multi-sensor image fusion for pansharpening in remote sensing [J ] . International Journal of Image and Data Fusion , 2010 , 1 ( 1 ): 25 - 45 .

TAO R , ZHAO X D , LI W , et al . Hyperspectral anomaly detection by fractional Fourier entropy [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2019 , 12 ( 12 ): 4920 - 4929 .

WINOGRAD S . On computing the discrete Fourier transform [J ] . Mathematics of Computation , 1978 , 32 ( 141 ): 175 - 199 .

TANG Y Y , LU Y , YUAN H L . Hyperspectral image classification based on three-dimensional scattering wavelet transform [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2015 , 53 ( 5 ): 2467 - 2480 .

LI W , DU Q . Collaborative representation for hyperspectral anomaly detection [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2015 , 53 ( 3 ): 1463 - 1474 .

MEI S H , YUAN X , JI J Y , et al . Hyperspectral image spatial super-resolution via 3D full convolutional neural network [J ] . Remote Sensing , 2017 , 9 ( 11 ): 1139 .

DU B , ZHANG L P . A discriminative metric learning based anomaly detection method [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2014 , 52 ( 11 ): 6844 - 6857 .

LI W , DU Q . A survey on representation-based classification and detection in hyperspectral remote sensing imagery [J ] . Pattern Recognition Letters , 2016 , 83 : 115 - 123 .

REED I S , YU X . Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution [J ] . IEEE Transactions on Acoustics, Speech, and Signal Processing , 1990 , 38 ( 10 ): 1760 - 1770 .

CHANG C I , CHIANG S S . Anomaly detection and classification for hyperspectral imagery [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2002 , 40 ( 6 ): 1314 - 1325 .

LI W , DU Q . Decision fusion for dual-window-based hyperspectral anomaly detector [J ] . Journal of Applied Remote Sensing , 2015 , 9 ( 1 ): 097297 .

LIU W M , CHANG C I . Multiple-window anomaly detection for hyperspectral imagery [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2013 , 6 ( 2 ): 644 - 658 .

XU Y , WU Z B , LI J , et al . Anomaly detection in hyperspectral images based on low-rank and sparse representation [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 4 ): 1990 - 2000 .

SUN W W , TIAN L , XU Y , et al . A randomized subspace learning based anomaly detector for hyperspectral imagery [J ] . Remote Sensing , 2018 , 10 ( 3 ): 417 .

SUN W W , YANG G , LI J L , et al . Randomized subspace-based robust principal component analysis for hyperspectral anomaly detection [J ] . Journal of Applied Remote Sensing , 2018 , 12 ( 1 ): 015015 .

YUAN Y , MA D D , WANG Q . Hyperspectral anomaly detection by graph pixel selection [J ] . IEEE Transactions on Cybernetics , 2016 , 46 ( 12 ): 3123 - 3134 .

SUN W W , DU Q . Graph-regularized fast and robust principal component analysis for hyperspectral band selection [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 6 ): 3185 - 3195 .

AMRANI N , SERRA-SAGRISTÀ J , LAPARRA V , et al . Regression wavelet analysis for lossless coding of remote-sensing data [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 9 ): 5616 - 5627 .

QIAO T , REN J C , WANG Z , et al . Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2017 , 55 ( 1 ): 119 - 133 .

CANDAN C , KUTAY M A , OZAKTAS H M . The discrete fractional Fourier transform [J ] . IEEE Transactions on Signal Processing , 2000 , 48 ( 5 ): 1329 - 1337 .

CHEN S , ZHANG S N , ZHAO H C , et al . A new chirp scaling algorithm for highly squinted missile-borne SAR based on FrFT [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2015 , 8 ( 8 ): 3977 - 3987 .

WANG S H , ZHANG Y D , YANG X J , et al . Pathological brain detection by a novel image feature—Fractional Fourier entropy [J ] . Entropy , 2015 , 17 ( 12 ): 8278 - 8296 .

LIAO W Z , VAN COILLIE F , GAO L R , et al . Deep learning for fusion of APEX hyperspectral and full-waveform LiDAR remote sensing data for tree species mapping [J ] . IEEE Access , 2018 , 6 : 68716 - 68729 .

MATASCI G , VOLPI M , KANEVSKI M , et al . Semisupervised transfer component analysis for domain adaptation in remote sensing image classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2015 , 53 ( 7 ): 3550 - 3564 .

YU S Q , JIA S , XU C Y . Convolutional neural networks for hyperspectral image classification [J ] . Neurocomputing , 2017 , 219 : 88 - 98 .

LUAN S Z , CHEN C , ZHANG B C , et al . Gabor convolutional networks [J ] . IEEE Transactions on Image Processing , 2018 , 27 ( 9 ): 4357 - 4366 .

ZHAO X , ZHANG M , TAO R , et al . Fractional Fourier image transformer for hyperspectral and LiDAR data classification [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2022 : 1 - 13 .

SHI J , ZHAO Y N , XIANG W , et al . Deep scattering network with fractional wavelet transform [J ] . IEEE Transactions on Signal Processing , 2021 , 69 : 4740 - 4757 .

CAMPBELL J B , PÉREZ F , WANG Q , et al . Remote vibration estimation using displaced-phase-center antenna SAR for strong clutter environments [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 5 ): 2735 - 2747 .

WANG Y , CHEN X F . 3-D interferometric inverse synthetic aperture radar imaging of ship target with complex motion [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 7 ): 3693 - 3708 .

SAXENA N , SHARMA K K . Pansharpening scheme using filtering in two-dimensional discrete fractional Fourier transform [J ] . IET Image Processing , 2018 , 12 ( 6 ): 1013 - 1019 .

WANG Q , GUO Q , ZHOU J Y , et al . Nonlinear joint fractional Fourier transform correlation for target detection in hyperspectral image [J ] . Optics & Laser Technology , 2012 , 44 ( 6 ): 1897 - 1904 .

LEE-THORP J , AINSLIE J , ECKSTEIN I , et al . FNet: Mixing tokens with Fourier transforms [EB/OL ] . ( 2021-05-09 )[ 2022-10 ] . https://arxiv.org/abs/2105.03824 https://arxiv.org/abs/2105.03824 .

DOSOVITSKIY A , BEYER L , KOLESNIKOV A , et al . An image is worth 16 x 16 words: Transformers for image recognition at scale[EB/OL ] . ( 2020-10-22 )[ 2022-10 ] . https://arxiv.org/abs/2010.11929 https://arxiv.org/abs/2010.11929 .

ZHAO X D , TAO R , LI W , et al . Joint classification of hyperspectral and LiDAR data using hierarchical random walk and deep CNN architecture [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2020 , 58 ( 10 ): 7355 - 7370 .

ZHANG M M , LI W , DU Q . Collaborative classification of hyperspectral and visible images with convolutional neural network [J ] . Journal of Applied Remote Sensing , 2017 , 11 ( 4 ): 042607 .

SCHMITT M , ZHU X X . Data Fusion and remote sensing: An ever-growing relationship [J ] . IEEE Geoscience and Remote Sensing Magazine , 2016 , 4 ( 4 ): 6 - 23 .

KHODADADZADEH M , LI J , PRASAD S , et al . Fusion of hyperspectral and LiDAR remote sensing data using multiple feature learning [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2015 , 8 ( 6 ): 2971 - 2983 .

HÄNSCH R , HELLWICH O . Fusion of multispectral LiDAR, hyperspectral, and RGB data for urban land cover classification [J ] . IEEE Geoscience and Remote Sensing Letters , 2021 , 18 ( 2 ): 366 - 370 .

MATSUKI T , YOKOYA N , IWASAKI A . Hyperspectral tree species classification of Japanese complex mixed forest with the aid of LiDAR data [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2015 , 8 ( 5 ): 2177 - 2187 .

KHODADADZADEH M , CUARTERO A , LI J , et al . Fusion of hyperspectral and LiDAR data using generalized composite kernels: A case study in Extremadura, Spain [C ] // IEEE International Geoscience and Remote Sensing Symposium . Milan : IEEE , 2015 : 61 - 64 .

MALLET C , BRETAR F . Full-waveform topographic lidar: State-of-the-art [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2009 , 64 ( 1 ): 1 - 16 .

JUNG J , PASOLLI E , PRASAD S , et al . A framework for land cover classification using discrete return LiDAR data: Adopting pseudo-waveform and hierarchical segmentation [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2014 , 7 ( 2 ): 491 - 502 .

LIAO W Z , BELLENS R , PIŽURICA A , et al . Graph-based feature fusion of hyperspectral and LiDAR remote sensing data using morphological features [C ] // IEEE International Geoscience and Remote Sensing Symposium . Victoria : IEEE , 2013 : 4942 - 4945 .

LIAO W Z , BELLENS R , PIŽURICA A , et al . Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data [C ] // IEEE Geoscience and Remote Sensing Symposium . Quebec City : IEEE , 2014 : 1241 - 1244 .

ZHAO X D , TAO R , LI W , et al . Fractional Gabor convolutional network for multisource remote sensing data classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 18 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Variable Horizon Multi-Directional Scanning Method for Time Series Anomaly Detection

Research on Dynamic Continuous Emotional Recognition of EEG Based on Improved TCNN Algorithm

Unsupervised Image Anomaly Detection Based on Constrained BidiRectional Distillation

Local Representation Few-Shot Learning for Hyperspectral Image Cross-Scene Classification

A Vehicle Trajectory Anomaly Detection Method Based on Noise Label Re-Weighting

Related Author

HUANG Yu-zhe

GUAN Yong-yuan

WEI Song-jie

JIE Li-lin

LIU Yong

WANG Ming-xun

ZOU Yang-meng

XU Yi-lu

Related Institution

School of Computer Science and Engineering, School of Cyber Science and Engineering, Nanjing University of Science and Technology

School of Instrument Science and Optoelectronic Engineering, Nanchang Hangkong University

School of Aviation Services and Music, Nanchang Hangkong University

School of Software, Jiangxi Agricultural University

School of Aeronautical Manufacturing and Mechanical Engineering, Nanchang Hangkong University

⁰