Clustering in remote sensing using an unsupervised neural network

Author

Acciani, G. ; Chiarantoni, E.

Author_Institution

Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, Italy

Volume

3

fYear

1996

fDate

13-16 May 1996

Firstpage

1446

Abstract

An unsupervised neural network based on a new neural unit is applied to the problem of clustering a data set of pixels drawn through remote sensing of a limited portion of the terrestrial surface. The aim of the partition is to split the sensed area into sets having roughly the same type of ground cover. After the partitioning a comparison with the ground truth has shown that the unsupervised net has been able to split accurately the given data set in subsets similar to the classes really observable with a fast convergence and a high resolution

Keywords

data analysis; geophysical signal processing; geophysical techniques; geophysics computing; image resolution; neural nets; remote sensing; unsupervised learning; IR; data set clustering; fast convergence; geophysical measurement technique; ground cover; ground truth; high resolution; image processing; land surface; neural unit; optical imaging; pixels; remote sensing; terrain mapping; terrestrial surface; unsupervised neural network; vegetation index; visible; Convergence; Energy resolution; Intelligent networks; Labeling; Neural networks; Remote sensing; Rough surfaces; Sensor phenomena and characterization; Sensor systems; Surface roughness;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrotechnical Conference, 1996. MELECON '96., 8th Mediterranean

Conference_Location

Bari

Print_ISBN

0-7803-3109-5

Type

conf

DOI

10.1109/MELCON.1996.551221

Filename

551221