|Table of Contents|

Sparse Graph Regularized Matrix Discriminant Analysis forHyperspectral Image Classification(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年01期
Page:
51-
Research Field:
·人工智能算法与应用专栏·
Publishing date:

Info

Title:
Sparse Graph Regularized Matrix Discriminant Analysis forHyperspectral Image Classification
Author(s):
Huang Xiaowei; Hang Renlong; Sun Yubao; Liu Qingshan
Jiangsu Key Laboratory of Big Data Analysis Technology,School of Information and Control,Nanjing University of Information Science and Technology,Nanjing 210044,China
Keywords:
hyperspectral image classification; spatial-spectral feature fusion; matrix-based discriminant analysis(MDA); sparse graph regularization
PACS:
TP751
DOI:
10.3969/j.issn.1001-4616.2019.01.009
Abstract:
In the field of hyperspectral image classification,the incorporation of spectral information and spatial information is one of the hot research topics. In this paper,a modified matrix-based discriminant analysis(MDA)model is proposed based on sparse graph regularization. The proposed model can not only combine the spectral information and spatial information effectively,but also sufficiently explore the wealth information from unlabeled samples,thus improving the classification performance. In order to verify the effectiveness of the proposed method,experiments have been conducted on two widely used hyperspectral images. The experimental results show that the performance of our method is superior as compared to other methods.

References:

[1] ZHAO J,ZHONG Y F,SHU H,et al. High-resolution image classification integrating spectral-spatial-location cues by conditional random fields[J]. IEEE transactions on image processing,2016,25(9):4033-4045.
[2]FAUVEL M,TARABALKA Y,BENEDIKTSSON J A,et al. Advances in spectral-spatial classification of hyperspectral images[J]. Proceedings of the IEEE,2013,101(3):652-675.
[3]FAUVEL M,BENEDIKTSSON J A,CHANUSSOT J,et al. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles[J]. IEEE transactions on geoscience and remote sensing,2008,46(11):3804-3814.
[4]CAMPS-VALLS G,GOMEZ-CHOVA L,MUNOZ-MARI J,et al. Composite kernels for hyperspectral image classification[J]. IEEE geoscience and remote sensing letters,2006,3(1):93-97.
[5]TARABALKA Y,BENEDIKTSSON J A,CHANUSSOT J,et al. Multiple spectral-spatial classification approach for hyperspectral data[J]. IEEE transactions on geoscience and remote sensing,2010,48(11):4122-4132.
[6]HANG R L,LIU Q S,SONG H H,et al. Matrix-based discriminant subspace ensemble for hyperspectral image spatial-spectral feature fusion[J]. IEEE transactions on geoscience and remote sensing,2016,54(2):783-794.
[7]HANG R L,LIU Q S,SUN Y B,et al. Robust matrix discriminative analysis for feature extraction from hyperspectral images[J]. IEEE journal of selected topics in applied earth observations and remote sensing,2017,10(5):2002-2011.
[8]GAO Y,JI R R,CUI P,et al. Hyperspectral image classification through bilayer graph-based learning[J]. IEEE transactions on image processing,2014,23(7):2769-2778.
[9]BAI J,XIANG S M,PAN C H. A graph-based classification method for hyperspectral images[J]. IEEE transactions on geoscience and remote sensing,2013,51(2):803-817.
[10]YANG L X,YANG S Y,JIN P L,et al. Semi-supervised hyperspectral image classification using spatio-spectral Laplacian support vector machine[J]. IEEE geoscience and remote sensing letters,2014,11(3):651-655.
[11]CHENG B,YANG J C,YAN S C,et al. Learning with l1-graph for image analysis[J]. IEEE transactions on image processing a publication of the IEEE signal processing society,2010,19(4):858-66.
[12]GU Y F,FENG K. L1-graph semisupervised learning for hyperspectral image classification[C]//Geoscience and Remote Sensing Symposium. Munich:IEEE,2012:1401-1404.
[13]YE J P,JANARDAN R,LI Q. Two-dimensional linear discriminant analysis[J]. Photogrammetric engineering and remote sensing,2004,5(6):1431-1441.

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Last Update: 2019-03-30