Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2016

01.06.2016

A Novel Spatial Analysis Method for Remote Sensing Image Classification

verfasst von: Jianqiang Gao, Lizhong Xu

Erschienen in: Neural Processing Letters | Ausgabe 3/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

A new and efficient classification model is introduced in this paper. The proposed model enjoys the information of null space of within-class and range space of within-class. And the proposed model aims at defining a reliable spatial analysis criterion for the remote sensing image, taking advantage of the differences in different areas. Finally, by incorporating fisher linear discriminant analysis and support vector machine (or K-nearest neighbor) classifier among image pixels, the model obtained more accurate classification results.
Vorheriger Artikel Reduced Multi-class Contour Preserving Classification
Nächster Artikel Face Recognition Using A Low Rank Representation Based Projections Method

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Literatur
1.
Gong P, Howarth PJ (1992) Frequency-based contextual classification and gray-level vector reduction for land-use identification. Photogramm Eng Remote Sens 58(4):423–437
2.
Kontoes C, Wilkinson GG, Burrill A, Goffredoa S, Megier J (1993) An experimental system for the integration of GIS data in knowledge-based image analysis for remote sensing of agriculture. Int J Geogr Inf Syst 7(3):247–262CrossRef
3.
Foody GM (1996) Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data. Int J Remote Sens 17(7):1317–1340CrossRef
4.
San Miguel-Ayanz J, Biging GS (1997) Comparison of single-stage and multi-stage classification approaches for cover type mapping with TM and SPOT data. Remote Sens Environ 59(1):92–104CrossRef
5.
Aplin P, Atkinson PM, Curran P (1999) Per-field classification of land use using the forthcoming very fine spatial resolution satellite sensors: problems and potential solutions. In: Atkinson PM, Tate NJ (eds) Advances in remote sensing and GIS analysis. Wiley, New York, pp 219–239
6.
Stuckens J, Coppin PR, Bauer ME (2000) Integrating contextual information with per-pixel classification for improved land cover classification. Remote Sens Environ 71(3):282–296CrossRef
7.
Franklin SE, Peddle DR, Dechka JA, Stenhouse GB (2002) Evidential reasoning with Landsat TM, DEM and GIS data for landcover classification in support of grizzly bear habitat mapping. Int J Remote Sens 23(21):4633–4652CrossRef
8.
Pal M, Mather PM (2003) An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ 86(4):554–565CrossRef
9.
Gallego FJ (2004) Remote sensing and land cover area estimation. Int J Remote Sens 25(15):3019–3047CrossRef
10.
Zhang R, Ma J (2008) An improved SVM method P-SVM for classification of remotely sensed data. Int J Remote Sens 29(20):6029–6036CrossRef
11.
Melgani F, Serpico SB (2002) A statistical approach to the fusion of spectral and spatio-temporal contextual information for the classification of remote-sensing images. Pattern Recognit Lett 23(9):1053–1061CrossRefMATH
12.
Bardossy A, Samaniego L (2002) Fuzzy rule-based classification of remotely sensed imagery. IEEE Trans Geosci Remote Sens 40(2):362–374CrossRef
13.
Bruzzone L, Cossu R (2002) A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps. IEEE Trans Geosci Remote Sens 40(9):1984–1996CrossRefMATH
14.
Hughes G (1968) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory 14(1):55–63CrossRef
15.
Quan JJ, Wen XB, Xu XQ (2008) Multiscale probabilistic neural network method for SAR image segmentation. Appl Math Comput 205(2):578–583MathSciNetMATH
16.
Zhang Y, Wu L, Neggaz N, Wang S, Wei G (2009) Remote-sensing image classification based on an improved probabilistic neural network. Sensors 9(9):7516–7539CrossRef
17.
Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262–3272MathSciNetCrossRef
18.
Yu J, Tao D, Li J, Cheng J (2014) Semantic preserving distance metric learning and applications. Inf Sci 281:674–686MathSciNetCrossRef
19.
Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019–2032MathSciNetCrossRef
20.
21.
Liu W, Tao D (2013) Multiview hessian regularization for image annotation. IEEE Trans Image Process 22(7):2676–2687MathSciNetCrossRef
22.
Liu W, Tao D, Cheng J, Tang Y (2014) Multiview hessian discriminative sparse coding for image annotation. Comput Vis Image Underst 118:50–60CrossRef
23.
Jolliffe IT (2002) Principal component analysis. Springer, BerlinMATH
24.
Schölkopf B, Smola A, Muller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319CrossRef
25.
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRef
26.
Dianat R, Kasaei S (2010) Dimension reduction of remote sensing images by incorporating spatial and spectral properties. AEU-Int J Electron Commun 64(8):729–732CrossRef
27.
Fauvel M, Chanussot J, Benediktsson JA (2012) A spatial-spectral kernel-based approach for the classification of remote-sensing images. Pattern Recognit 45(1):381–392CrossRef
28.
Fauvel M et al (2013) Advances in spectral-spatial classification of hyperspectral images. Proc IEEE 101(3):652–675CrossRef
29.
Liu J, Wu Z, Wei Z, Xiao L, Sun L (2013) Spatial-spectral kernel sparse representation for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 6(6):2462–2471CrossRef
30.
Camps-Valls G, Bruzzone L (2005) Kernel-based methods for hyperspectral image classification. IEEE Trans Geosci Remote Sens 43(6):1351–1362CrossRef
31.
Gao J, Fan L (2011) Kernel-based weighted discriminant analysis with QR decomposition and its application face recognition. WSEAS Trans Math 10(10):358–367MathSciNet
32.
Gao J, Li L, Fan L, Xu L (2013) An application of weighted kernel fuzzy discriminant analysis. Adv Comput Math Appl 2(4):329–338MathSciNet
33.
Gao J, Fan L, Li L, Xu L (2013) A practical application of kernel-based fuzzy discriminant analysis. Int J Appl Math Comput Sci 23(4):887–903MathSciNetCrossRefMATH
34.
Gao S, Tsang IWH, Chia LT (2010) Kernel sparse representation for image classification and face recognition, Computer Vision-ECCV 2010. Springer, Berlin, pp 1–14
35.
Gao J, Xu L, Shi A, Huang F (2014) A kernel-based block matrix decomposition approach for the classification of remotely sensed images. Appl Math Comput 228:531–545MathSciNet
36.
Ye J, Li Q (2005) A two-stage linear discriminant analysis via QR-decomposition. IEEE Trans Pattern Anal Mach Intell 27(6):929–941CrossRef
37.
Chen L, Liao HM, Ko M, Lin J, Yu G (2000) A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognit 33(10):1713–1726CrossRef
38.
Yu H, Yang J (2001) A direct LDA algorithm for high-dimensional data-with application to face recognition. Pattern Recognit 34(10):2067–2070CrossRefMATH
39.
Li X, Fei S, Zhang T (2009) Median MSD-based method for face recognition. Neurocomputing 72(16):3930–3934CrossRef
40.
Gao J, Fan L, Xu L (2013) Median null (Sw)-based method for face feature recognition. Appl Math Comput 219(12):6410–6419MathSciNetMATH
41.
Koç M, Barkana A (2011) A new solution to one sample problem in face recognition using FLDA. Appl Math Comput 217(24):10368–10376MathSciNetMATH
42.
Pan Y, Wu J, Huang H, Liu J (2012) Spectral regression discriminant analysis for hyperspectral image classification. ISPRS-Int Arch Photogramm Remote Sens Spat Inf Sci 1:503–508CrossRef
43.
Ghosh A, Mishra NS, Ghosh S (2011) Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf Sci 181:699–715CrossRef
44.
Bandos TV, Bruzzone L, Camps-Valls G (2009) Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Trans Geosci Remote Sens 47(3):862–873CrossRef
45.
Geva S, Sitte J (1991) Adaptive nearest neighbor pattern classification. IEEE Trans Neural Netw 2(2):318–322CrossRef
46.
Cover TM, Hart FE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory IT–13(1):21–27CrossRefMATH
47.
Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities with application in pattern recognition. IEEE Trans Electron Comput 14(3):326–334CrossRefMATH
48.
Schölkopf B, Smola A (2002) Learning with kernels-support vector machines, regularization, optimization and beyond. MIT Press, Cambridge
49.
Fauvel M, Benediktsson JA, Chanussot J, Sveinsson JR (2008) Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans Geosci Remote Sens 46(11):3804–3814CrossRef
50.
Tuia D, Camps-Valls G (2011) Urban image classification with semisupervised multiscale cluster kernels. IEEE J Sel Top Appl Earth Obs Remote Sens 4(1):65–74CrossRef
51.
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66(3):247–259CrossRef
52.
Sun C, Lam KM (2013) Multiple-kernel multiple-instance similarity features for efficient visual object detection. IEEE Trans Image Process 22(8):3050–3061MathSciNetCrossRef
53.
Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. Johns Hopkins University Press, BaltimoreMATH
54.
55.
Metadaten
Titel
A Novel Spatial Analysis Method for Remote Sensing Image Classification
verfasst von
Jianqiang Gao
Lizhong Xu
Publikationsdatum
01.06.2016
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2016
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-015-9447-0

Weitere Artikel der Ausgabe 3/2016

Neural Processing Letters 3/2016 Zur Ausgabe

OriginalPaper

Neural Networks Based PID Control of Bidirectional Inductive Power Transfer System

OriginalPaper

Coordinate Descent Algorithm for Ramp Loss Linear Programming Support Vector Machines

OriginalPaper

Reduced Multi-class Contour Preserving Classification

OriginalPaper

A New and Fast Implementation of Orthogonal LDA Algorithm and Its Incremental Extension

OriginalPaper

Constrained Self Organizing Maps for Data Clusters Visualization

OriginalPaper

Lagrange -Stability and Exponential -Convergence for Stochastic Cohen–Grossberg Neural Networks with Time-Varying Delays

Neuer Inhalt

    Bildnachweise