Abstract

The use of remote sensed images in many applications of environmental monitoring,change detection, risks analysis, damage prevention, etc. is continuously growing.Classification of remote sensed images, exploited for the production of land cover maps,involves continuous efforts in the refinement of the employed methodologies. The pixel-wise approach, which considers the spectral information associated to each pixel in theimage, is the standard classification methodology. The continuous improving of spatialresolution in remote sensors requires the focus on what is around a single pixel with theintegration of "contextual" information. In order to produce more reliable land cover mapsfrom the classification of high resolution images, this paper analyzes the effectiveness ofthe integration of contextual information comparing two different pixel-wise techniques forits extraction: 1) the post-classification filtering with a Majority filter applied to the mapproduced by the standard Maximum Likelihood algorithm; 2) the segmentation algorithmSMAP. The results were compared. A GeoEye-1 image, exploited in the framework of theAsi-Morfeo project, was considered.

Autore Pugliese

• Lovergine Francesco , Pasquariello Guido

Tutti gli autori

• C. Tarantino; F. P. Lovergine; M. Adamo; G. Pasquariello

Titolo volume/Rivista

Rivista italiana di telerilevamento

Anno di pubblicazione

2011

ISSN

1129-8596

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

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Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile