A completely fuzzy classification chain for multispectral remote sensing images

Author

Gamba, Paolo ; Marazzi, Andrea ; Mecocci, Alessaiidro ; Savazzi, Pietro

Author_Institution

Dipartimento di Elettronica, Pavia Univ., Italy

Volume

4

fYear

1996

fDate

27-31 May 1996

Firstpage

2071

Abstract

In this work a new classification algorithm that uses FNP mixed with a pyramidal approach is proposed. The prototypes of each class are generated by means of FCM with a FNP initialization. The aim of the work is to improve the performances of the usual non parametric classifiers by extracting the maximum information from the training pixels and from the pixels to be classified. This is done by using both the high spatial-correlation between pixels and the confidence levels, given by the fuzzy algorithm. Results are presented that show the improvement obtained by applying the proposed method to multispectral image classification

Keywords

geophysical signal processing; geophysical techniques; image classification; remote sensing; FNP; classification algorithm; fuzzy classification chain; fuzzy nearest prototype; geophysical measurement technique; image classification; land surface; multidimensional signal processing; multispectral remote sensing; optical imaging; pyramidal approach; terrain mapping; training pixels; Fuzzy sets; Image classification; Image sensors; Infrared image sensors; Prototypes; Sensor phenomena and characterization; Stress; Utility programs;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1996. IGARSS '96. 'Remote Sensing for a Sustainable Future.', International

Conference_Location

Lincoln, NE

Print_ISBN

0-7803-3068-4

Type

conf

DOI

10.1109/IGARSS.1996.516891

Filename

516891