This work describes an application of fuzzy-logic concepts for land cover classification of multifrequency SAR images, either supervised or not, starting from a number of pixel features derived from the input SAR observations. Possible "a priori" knowledge coming from ground truth data may be used to initialize the procedure, but it is not mandatory. Pixel vectors composed by simple features derived from the backscatter coefficients of one or more observations are clustered iby means of an iterative procedure. The pixels of the scene are classified in base of a weighted Euclidean distance of the respective vector from the centroids representative of each class. The upgrade of the centroids is obtained through a membership function of the pixels concerning their degree of affinity to each class. Pixel features are weighted with optimized coefficients, whose computing is based on the LS algorithm by using the same previous fuzzy membership function.
In questo lavoro viene presentata un'applicazione della logica fuzzy per la classificazione di terreni agricoli a partire da parametri derivati da coefficienti di backscattering di immagini SAR a multifrequenza, funzionante in modalità supervisionata oppure totalmente automatica. Nel primo caso la procedura viene inizializzata mediante conoscenza "a priori" acquisita con misure di verità a terra. Il procedimento si basa sul raffinamento iterativo dei centroidi ottenuti raggruppando i vettori dei parametri d'ingresso. Gli elementi della scena vengono classificati considerando la distanza tra il rispettivo vettore e i centroidi ottenuti. La distanza considerata è di tipo euclideo pesata con coefficienti associati a ciascun ingresso con un procedimento a minimi quadrati. Essa consente di definire una funzione di membership relativa al grado di appartenenza dei vettori rispetto alle varie classi su cui si basa il raffinamento dei centroidi e dei coefficienti di pesatura.
A Fuzzy-Logic Based Classification of SAR Data through an Iterative Reclustering of Weighted Pixel Features
Bruno Aiazzi;Stefano Baronti;Massimo Bianchini;Giovanni Macelloni;Simonetta Paloscia
2003
Abstract
This work describes an application of fuzzy-logic concepts for land cover classification of multifrequency SAR images, either supervised or not, starting from a number of pixel features derived from the input SAR observations. Possible "a priori" knowledge coming from ground truth data may be used to initialize the procedure, but it is not mandatory. Pixel vectors composed by simple features derived from the backscatter coefficients of one or more observations are clustered iby means of an iterative procedure. The pixels of the scene are classified in base of a weighted Euclidean distance of the respective vector from the centroids representative of each class. The upgrade of the centroids is obtained through a membership function of the pixels concerning their degree of affinity to each class. Pixel features are weighted with optimized coefficients, whose computing is based on the LS algorithm by using the same previous fuzzy membership function.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.