基于结构化特征重构的高光谱图像分类

邢长达; 汪美玲; 徐雍倡; 王志胜

doi:10.12263/DZXB.20230077

您当前的位置：

首页 >

文章列表页 >

基于结构化特征重构的高光谱图像分类

学术论文 | 更新时间：2025-12-08

- 基于结构化特征重构的高光谱图像分类
- Structure-Wise Feature Reconstruction for Hyperspectral Image Classification
- 电子学报 2024年52卷第9期页码：3010-3022
- 作者机构：
  
  1.中国矿业大学信息与控制工程学院,江苏徐州 221116
  2.南京航空航天大学计算机科学与技术学院,江苏南京 211106
  3.南京航空航天大学自动化学院,江苏南京 211106
- 作者简介：
  
  [ "邢长达男，1989年11月出生于安徽省滁州市.现为中国矿业大学信息与控制工程学院准聘副教授，研究方向是高光谱图像智能分析，在国内外发表学术论文20余篇.E-mail: xingchangda@cumt.edu.cn" ]
  [ "汪美玲女，1988年1月出生于安徽省芜湖市.毕业于南京航空航天大学计算机科学与技术专业.现为南京邮电大学计算机学院讲师，研究方向为数据挖掘与人工智能. E-mail: mely@njupt.edu.cn" ]
  [ "徐雍倡男，1996年11月出生于山东省临沂市.现为南京航空航天大学博士研究生.主要研究方向为智能控制技术. E-mail: xuyongchang@nuaa.edu.cn" ]
  [ "王志胜男，1970年6月出生于湖北省荆门市.现在南京航空航天大学自动化学院教授、博士生导师.主要研究方向为图像处理、人工智能技术、复杂系统控制等. E-mail: wangzhisheng@nuaa.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62101247;62106104);中国博士后科学基金(2022T150320);中央引导地方科技发展专项资金项目(2021Szvup063)
- DOI：10.12263/DZXB.20230077
  中图分类号： TP751
- 收稿：2023-01-30，
  
  修回：2023-23-25，
  
  纸质出版：2024-09-25
- 稿件说明：
移动端阅览
邢长达, 汪美玲, 徐雍倡, 等. 基于结构化特征重构的高光谱图像分类[J]. 电子学报, 2024, 52(09): 3010-3022.

XING Chang-da, WANG Mei-ling, XU Yong-chang, et al. Structure-Wise Feature Reconstruction for Hyperspectral Image Classification[J]. Acta Electronica Sinica, 2024, 52(09): 3010-3022.
邢长达, 汪美玲, 徐雍倡, 等. 基于结构化特征重构的高光谱图像分类[J]. 电子学报, 2024, 52(09): 3010-3022. DOI：10.12263/DZXB.20230077

XING Chang-da, WANG Mei-ling, XU Yong-chang, et al. Structure-Wise Feature Reconstruction for Hyperspectral Image Classification[J]. Acta Electronica Sinica, 2024, 52(09): 3010-3022. DOI：10.12263/DZXB.20230077

摘要

特征提取是高光谱图像分类的关键.现有分类方法在特征提取时，往往忽略特征的信息保有量和空间分布等因素，导致输出的特征可能面临低信息保有量与无序分布等问题，预测结果不佳.为此，本文提出一种基于结构化特征重构的高光谱图像分类方法，能够有效地减少特征提取过程中信息丢失，提高信息保有量，并充分考虑特征的空间分布，增强特征的判别性.借鉴重构思想以及自表达理论，建立结构特征重构的特征表示模型，可提升图像信息的利用率，并描述反映有序分布的结构信息.针对建立的多变量模型，设计一种基于交替更新的优化策略来求解模型.利用支持向量机来对特征进行分类计算和标签预测.利用Salinas、Pavia Center、Botswana以及Houston数据进行实验验证，结果表明，本文算法优于现有的分类模型，在OA（Overall Accuracy）、AA（Average Accuracy）以及Kappa系数等指标上平均提升了2.6%、3.9%、3.3%.

Abstract

Feature extraction is a key operation for hyperspectral image (HSI) classification. For current classification approaches

they usually ignore the information preservation and spatial distribution in feature extraction

which may export features with low information utilization and disordered distribution

generating unsatisfactory prediction results. To remedy such deficiencies

a novel method based on structure-wise feature reconstruction is proposed for the HSI classification. This method can reduce the information loss and improve the information preservation during the process of feature extraction. In addition

the distribution is also fully considered to enhance the discriminability and separability. In this proposed method

considering the reconstruction idea and the self-expression theory

a structure-wise feature reconstruction model is constructed to extract the features of the HSI

which can improve the information utilization of original information from the HSI and describe the structure reflecting the well-ordered distribution. Here

an optimization with alternative updating is presented to solve the above constructed model. The support vector machine is finally used to classify the extracted features and predict the labels of the HSI. The Salinas

Pavia Center

Botswana

and Houston datasets are used for experimental validation. Results show that the proposed method achieves the better classification performance compared with some state-of-the-art approaches

which is averagely higher 2.6%

3.9%

3.3% at OA (Overall Accuracy)

AA (Average Accuracy)

and Kappa indexes.

关键词

Keywords

references

GHAMISI P , PLAZA J , CHEN Y S , et al . Advanced spectral classifiers for hyperspectral images: A review [J ] . IEEE Geoscience and Remote Sensing Magazine , 2017 , 5 ( 1 ): 8 - 32 .

黄鸿 , 王丽华 , 石光耀 . 面向高光谱遥感影像分类的监督多流形鉴别嵌入方法 [J ] . 电子学报 , 2020 , 48 ( 6 ): 1099 - 1107 .

HUANG H , WANG L H , SHI G Y . Supervised multi-manifold discriminant embedding method for hyperspectral remote sensing image classification [J ] . Acta Electronica Sinica , 2020 , 48 ( 6 ): 1099 - 1107 . (in Chinese)

刘启超 , 肖亮 , 刘芳 , 等 . SSCDenseNet: 一种空-谱卷积稠密网络的高光谱图像分类算法 [J ] . 电子学报 , 2020 , 48 ( 4 ): 751 - 762 .

LIU Q C , XIAO L , LIU F , et al . SSCDenseNet: A spectral-spatial convolutional dense network for hyperspectral image classification [J ] . Acta Electronica Sinica , 2020 , 48 ( 4 ): 751 - 762 . (in Chinese)

HE L , LI J , LIU C Y , et al . Recent advances on spectral-spatial hyperspectral image classification: An overview and new guidelines [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 3 ): 1579 - 1597 .

BENEDIKTSSON J A , PESARESI M , ARNASON K . Classification and feature extraction for remote sensing images from urban areas based on morphological transformations [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2003 , 41 ( 9 ): 1940 - 1949 .

BENEDIKTSSON J A , PALMASON J A , SVEINSSON J R . Classification of hyperspectral data from urban areas based on extended morphological profiles [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2005 , 43 ( 3 ): 480 - 491 .

LIAO W Z , BELLENS R , PIZURICA A , et al . Classification of hyperspectral data over urban areas using directional morphological profiles and semi-supervised feature extraction [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2012 , 5 ( 4 ): 1177 - 1190 .

FANG L Y , WANG C , LI S T , et al . Hyperspectral image classification via multiple-feature-based adaptive sparse representation [J ] . IEEE Transactions on Instrumentation and Measurement , 2017 , 66 ( 7 ): 1646 - 1657 .

ROH B , SHIN W , KIM I , et al . Spatially consistent representation learning [C ] // 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2021 : 1144 - 1153 .

SCHOLKOPF B , LOCATELLO F , BAUER S , et al . Toward causal representation learning [J ] . Proceedings of the IEEE , 2021 , 109 ( 5 ): 612 - 634 .

XING C D , WANG M L , DONG C , et al . Joint sparse-collaborative representation to fuse hyperspectral and multispectral images [J ] . Signal Processing , 2020 , 173 : 107585 .

CHEN Y , NASRABADI N M , TRAN T D . Hyperspectral image classification using dictionary-based sparse representation [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2011 , 49 ( 10 ): 3973 - 3985 .

ZHANG X R , WENG P , FENG J , et al . Spatial-spectral classification based on group sparse coding for hyperspectral image [C ] // 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS . Piscataway : IEEE , 2013 : 1745 - 1748 .

ZHANG H Y , LI J Y , HUANG Y C , et al . A nonlocal weighted joint sparse representation classification method for hyperspectral imagery [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2014 , 7 ( 6 ): 2056 - 2065 .

HE Z , LIU L , DENG R R , et al . Low-rank group inspired dictionary learning for hyperspectral image classification [J ] . Signal Processing , 2016 , 120 : 209 - 221 .

CHEN Z , WU X J , KITTLER J . Low-rank discriminative least squares regression for image classification [J ] . Signal Processing , 2020 , 173 : 107485 .

XING C D , WANG M L , WANG Z S , et al . Diagonalized low-rank learning for hyperspectral image classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 12 .

LI Y S , TANG H J , XIE W X , et al . Multidimensional local binary pattern for hyperspectral image classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 13 .

JIA S , DENG B , ZHU J S , et al . Local binary pattern-based hyperspectral image classification with superpixel guidance [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 2 ): 749 - 759 .

CHEN Y S , LIN Z H , ZHAO X , et al . Deep learning-based classification of hyperspectral data [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2014 , 7 ( 6 ): 2094 - 2107 .

CHEN Y S , ZHAO X , JIA X P . Spectral-spatial classification of hyperspectral data based on deep belief network [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2015 , 8 ( 6 ): 2381 - 2392 .

XU Y H , DU B , ZHANG F , et al . Hyperspectral image classification via a random patches network [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2018 , 142 : 344 - 357 .

CAO X Y , YAO J , XU Z B , et al . Hyperspectral image classification with convolutional neural network and active learning [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2020 , 58 ( 7 ): 4604 - 4616 .

LI W , WU G D , ZHANG F , et al . Hyperspectral image classification using deep pixel-pair features [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2017 , 55 ( 2 ): 844 - 853 .

YANG X F , CAO W J , LU Y , et al . Hyperspectral image transformer classification networks [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 15 .

JI P , ZHANG T , LI H D , et al . Deep subspace clustering networks [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . Long Beach : Curran Associates Inc , 2017 : 23 - 32 .

SORENSEN D C , ANTOULAS A C . The Sylvester equation and approximate balanced reduction [J ] . Linear Algebra and Its Applications , 2002 , 351/352 : 671 - 700 .

LI S T , DIAN R W , FANG L Y , et al . Fusing hyperspectral and multispectral images via coupled sparse tensor factorization [J ] . IEEE Transactions on Image Processing , 2018 , 27 ( 8 ): 4118 - 4130 .

WATSON G S . Linear least squares regression [J ] . The Annals of Mathematical Statistics , 1967 , 38 ( 6 ): 1679 - 1699 .

DONOHO D L . De-noising by soft-thresholding [J ] . IEEE Transactions on Information Theory , 1995 , 41 ( 3 ): 613 - 627 .

WU Y F , YANG X H , PLAZA A , et al . Approximate computing of remotely sensed data: SVM hyperspectral image classification as a case study [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2016 , 9 ( 12 ): 5806 - 5818 .

万升 , 杨健 , 宫辰 . 基于图神经网络的高光谱图像分类研究进展 [J ] . 电子学报 , 2023 , 51 ( 6 ): 1687 - 1709 .

WAN S , YANG J , GONG C . Advances of hyperspectral image classification based on graph neural networks [J ] . Acta Electronica Sinica , 2023 , 51 ( 6 ): 1687 - 1709 . (in Chinese)

WANG Z , BOVIK A C , SHEIKH H R , et al . Image quality assessment: From error visibility to structural similarity [J ] . IEEE Transactions on Image Processing , 2004 , 13 ( 4 ): 600 - 612 .

SHEIKH H R , BOVIK A C , DE VECIANA G . An information fidelity criterion for image quality assessment using natural scene statistics [J ] . IEEE Transactions on Image Processing , 2005 , 14 ( 12 ): 2117 - 2128 .

VAN DER MAATEN L , HINTON G . Visualizing data using t-SNE [J ] . Journal of Machine Learning Research , 2008 , 9 : 2579 - 2625 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于轻量级全连接张量映射网络的高光谱图像分类方法

融合句嵌入模型和代码特征的补丁验证方法

基于图神经网络的高光谱图像分类研究进展

基于改进时域多尺度散布熵与支持向量机的转辙机故障诊断