Share Email Print
cover

Proceedings Paper

A new system to perform unsupervised and supervised classification of satellite images from Google Maps
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In this paper, we describe a new system for unsupervised and supervised classification of satellite images from Google Maps. The system has been developed using the SwingX-WS library, and incorporates functionalities such as unsupervised classification of image portions selected by the user (at the maximum zoom level) using ISODATA and k-Means, and supervised classification using the Minimum Distance and Maximum Likelihood, followed by spatial post-processing based on majority voting. Selected regions in the classified portion are used to train a maximum likelihood classifier able to map larger image areas in a manner transparent to the user. The system also retrieves areas containing regions similar to those already classified. An experimental validation of the proposed system has been conducted by comparing the obtained classification results with those provided by commercial software, such as the popular Research Systems ENVI package.

Paper Details

Date Published: 24 August 2010
PDF: 10 pages
Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 781010 (24 August 2010); doi: 10.1117/12.863243
Show Author Affiliations
Sergio Bernabé, Univ. of Extremadura (Spain)
Antonio Plaza, Univ. of Extremadura (Spain)


Published in SPIE Proceedings Vol. 7810:
Satellite Data Compression, Communications, and Processing VI
Bormin Huang; Antonio J. Plaza; Joan Serra-Sagristà; Chulhee Lee; Yunsong Li; Shen-En Qian, Editor(s)

© SPIE. Terms of Use
Back to Top