Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 8/2019

21-11-2017 | Original Article

Semi-supervised fuzzy neighborhood preserving analysis for feature extraction in hyperspectral remote sensing images

Authors: Hasan Ali Akyürek, Barış Koçer

Published in: Neural Computing and Applications | Issue 8/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Semi-supervised feature extraction methods are an important focus of interest in data mining and machine learning areas. These methods are improved methods based on learning from a combination of labeled and unlabeled data. In this study, a semi-supervised feature extraction method called as semi-supervised fuzzy neighborhood preserving analysis (SFNPA) is proposed to improve the classification accuracy of hyperspectral remote sensing images. The proposed method combines the principal component analysis (PCA) method, which is an unsupervised feature extraction method, and the supervised fuzzy neighborhood preserving analysis (FNPA) method and increases the classification accuracy by using a limited number of labeled data. Experimental results on four popular hyperspectral remote sensing datasets show that the proposed method significantly improves classification accuracy on hyperspectral remote sensing images compared to the well-known semi-supervised dimension reduction methods.
previous article Financial time series prediction using distributed machine learning techniques
next article Hypothesis transfer learning based on structural model similarity

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Literature
1.
Fong M (2007) Dimension reduction on hyperspectral images. Univ California, Los Angeles
2.
Bruzzone L, Persello C (2009) A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability. IEEE Trans Geosci Remote Sens 47(9):3180–3191CrossRef
3.
Rodarmel C, Shan J (2002) Principal component analysis for hyperspectral image classification. Surv Land Inf Sci 62(2):115
4.
Chen G, Qian S-E (2007) Dimensionality reduction of hyperspectral imagery using improved locally linear embedding. J Appl Remote Sens 1(1):013509–013510MathSciNetCrossRef
5.
Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: NIPS, pp 585–591
6.
Zhang T, Yang J, Zhao D, Ge X (2007) Linear local tangent space alignment and application to face recognition. Neurocomputing 70(7):1547–1553CrossRef
7.
He X, Cai D, Yan S, Zhang H-J (2005) Neighborhood preserving embedding. In: Tenth IEEE international conference on computer vision (ICCV’05), vol 1. IEEE, pp 1208–1213
8.
He X, Niyogi P (2004) Locality preserving projections. In: Advances in neural information processing systems 16. MIT Press, pp 153–160
9.
Z-y Zhang, H-y Zha (2004) Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. J Shanghai Univ 8(4):406–424 (English Edition) MathSciNetCrossRef
10.
Chang C-I, Ren H (2000) An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery. IEEE Trans Geosci Remote Sens 38(2):1044–1063CrossRef
11.
Du Q (2007) Modified Fisher’s linear discriminant analysis for hyperspectral imagery. IEEE Geosci Remote Sens Lett 4(4):503CrossRef
12.
Kuo B-C, Chang C-H, Sheu T-W, Hung C-C (2005) Feature extractions using labeled and unlabeled data. In: Proceedings of 2005 IEEE international geoscience and remote sensing symposium, 2005. IGARSS’05, IEEE, pp 1257–1260
13.
Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory, ACM, pp 92–100
14.
Vapnik VN, Vapnik V (1998) Statistical learning theory, vol 1. Wiley, New YorkMATH
15.
Bruzzone L, Chi M, Marconcini M (2006) A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Trans Geosci Remote Sens 44(11):3363–3373CrossRef
16.
17.
Camps-Valls G, Marsheva TVB, Zhou D (2007) Semi-supervised graph-based hyperspectral image classification. IEEE Trans Geosci Remote Sens 45(10):3044–3054CrossRef
18.
Zhu X (2008) Semi-supervised learning literature survey, Department of Computer Sciences, University of Wisconsin. Madison, 2008 (Technical Report, 1530)
19.
Wu X-H, Zhou J-J (2006) Fuzzy discriminant analysis with kernel methods. Pattern Recogn 39(11):2236–2239CrossRefMATH
20.
Ye J (2005) Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J Mach Learn Res 6:483–502MathSciNetMATH
21.
Ye J, Li T, Xiong T, Janardan R (2004) Using uncorrelated discriminant analysis for tissue classification with gene expression data. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 1(4):181–190CrossRef
22.
Qiao J, Yin H (2013) Optimizing kernel function with applications to kernel principal analysis and locality preserving projection for feature extraction. J Inf Hiding Multimed Signal Process 4(4):280–290
23.
Li J-B, Pan J-S, Chen S-M (2011) Kernel self-optimized locality preserving discriminant analysis for feature extraction and recognition. Neurocomputing 74(17):3019–3027CrossRef
24.
25.
26.
27.
Khushaba RN, Al-Ani A, Al-Jumaily A (2010) Orthogonal fuzzy neighborhood discriminant analysis for multifunction myoelectric hand control. IEEE Trans Biomed Eng 57(6):1410–1419CrossRef
28.
Khushaba RN, Kodagoda S, Liu D, Dissanayake G (2011) Electromyogram (EMG) based fingers movement recognition using neighborhood preserving analysis with QR-decomposition. In: 2011 seventh international conference on intelligent sensors, sensor networks and information processing (ISSNIP), 2011. IEEE, pp 1–105
29.
30.
31.
32.
Gader P, Zare A, Close R, Aitken J, Tuell G (2013) Muufl gulfport hyperspectral and lidar airborne data set. Univ. Florida, Gainesville, FL, USA, Tech. Rep. REP-2013-570
33.
Du X, Zare A (2017) Technical report: scene label ground truth map for MUUFL Gulfport Data Set. University of Florida, Gainesville
34.
35.
36.
37.
38.
Rosner B, Glynn RJ, Lee MLT (2006) The Wilcoxon signed rank test for paired comparisons of clustered data. Biometrics 62(1):185–192MathSciNetCrossRefMATH
39.
Jolliffe I (2002) Principal component analysis. Wiley, New YorkMATH
40.
Sugiyama M, Idé T, Nakajima S, Sese J (2010) Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Mach Learn 78(1–2):35–61MathSciNetCrossRef
41.
Cai D, He X, Han J (2007) Semi-supervised discriminant analysis. In: IEEE 11th international conference on computer vision. ICCV 2007, IEEE, pp 1–7
42.
Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27
43.
Metadata
Title
Semi-supervised fuzzy neighborhood preserving analysis for feature extraction in hyperspectral remote sensing images
Authors
Hasan Ali Akyürek
Barış Koçer
Publication date
21-11-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3279-y

Other articles of this Issue 8/2019

Neural Computing and Applications 8/2019 Go to the issue

Original Article

Improved algorithms for interference suppression in non-contiguous orthogonal frequency division multiplexing-based cognitive radio systems

Original Article

The application of an effective cuckoo search algorithm for optimal scheduling of hydrothermal system considering transmission constraints

Original Article

A novel VIKOR method with an application to multiple criteria decision analysis for hospital-based post-acute care within a highly complex uncertain environment

Brain- Inspired computing and Machine learning for Brain Health

An improved TLBO with logarithmic spiral and triangular mutation for global optimization

Original Article

Numerical solutions of fuzzy fractional diffusion equations by an implicit finite difference scheme

Original Article

Intelligent tutoring system model based on fuzzy logic and constraint-based student model

Premium Partner

    Image Credits