Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloSAR image classification through information-theoretic textural features, MRF segmentation, and object-oriented learning vector quantization
Anno di pubblicazione2014
FormatoCartaceo
Autore/iD'Elia C.; Ruscino S.; Abbate M.; Aiazzi B.; Baronti S.; Alparone L.
Affiliazioni autoriDepartment of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino (FR), Italy; Institute of Applied Physics (IFAC), CNR Research Area of Florence, 50019 Florence, Italy; Department of Information Engineering, University of Florence, 50139 Florence, Italy
Autori CNR e affiliazioni
  • LUCIANO ALPARONE
  • BRUNO AIAZZI
  • STEFANO BARONTI
Lingua/e
  • inglese
AbstractSegmentation of optical images may be obtained through algorithms based on image prior models that exploit the spatial dependencies of land covers. In synthetic aperture radar (SAR) images, speckle conceals such spatial dependencies and segmentation algorithms suitable for optical images may become ineffective. Textural features may be used to emphasize spatial dependencies in the data and hence to improve segmentation. Once segmentation has been accomplished, a number of shapes is available. In this paper, the problem is tackled through the joint use of information-theoretic (IT) SAR features, of a segmentation algorithm based on tree structured Markov random fields (TS-MRFs), and of object-oriented classification achieved through learning vector quantization (LVQ). The proposed system works with one or more coregistered images, not necessarily all SAR, and one or more spatial maps of pixel features derived from each input image. A unique partition into connected regions, or segments, is achieved from the plurality of input channels, either images or feature maps. From each segment, representing a shape, geometric, radiometric, and textural parameters are extracted and fed to an LVQ classifier, trained through a partial reference ground truth (GT) of the scene. Classification results on a textured SAR image of a city and its surroundings validate the proposed object-oriented approach. Good performances can be achieved with small sizes of training sets, but they can be improved by using a decision fusion through majority voting (MV) of the outcomes of several experiments. © 2008-2012 IEEE.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da1116
Pagine a1126
Pagine totali11
RivistaIEEE journal of selected topics in applied earth observations and remote sensing (Online)
Attiva dal 2008
Editore: Institute of Electrical and Electronics Engineers, Inc. - New York, N.Y.
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 2151-1535
Titolo chiave: IEEE journal of selected topics in applied earth observations and remote sensing (Online)
Titolo proprio: IEEE journal of selected topics in applied earth observations and remote sensing (Online)
Titolo abbreviato: IEEE j. sel. top. appl. earth obs. remote sens. (Online)
Titoli alternativi:
  • Selected topics in applied earth observations and remote sensing, IEEE journal of (Online)
  • IEEE journal of selected topics in applied earth observations & remote sensing (Online)
  • Journal of selected topics in applied earth observations and remote sensing (Online)
  • Institute of Electrical and Electronic Engineers journal of selected topics in applied earth observations and remote sensing (Online)
Numero volume della rivista7
Fascicolo della rivista4
DOI10.1109/JSTARS.2014.2304700
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84899978984)
  • ISI Web of Science (WOS) (Codice:WOS:000335390000011)
Parole chiaveArtificial neural network (ANN), learning vector quantization (LVQ), segmentation, synthetic aperture radar (SAR) images, textural features, thematic classification
Link (URL, URI)http://www.scopus.com/inward/record.url?eid=2-s2.0-84899978984&partnerID=q2rCbXpz
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IFAC — Istituto di fisica applicata "Nello Carrara"
Moduli/Attività/Sottoprogetti CNR
  • ICT.P10.005.001 : Sistemi, metodi di elaborazione ed applicazioni di telerilevamento aerospaziale
Progetti Europei-
Allegati
Articolo pubblicato su rivista internazionale (documento privato )
Tipo documento: application/pdf

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Editore
  • IEEE - Institute of Electrical and Electronics Engineers, Piscataway, N.J. (Stati Uniti d'America)