
Proceedings Paper
Evaluation of different fitness functions integrated with genetic algorithm on unsupervised classification of satellite imagesFormat | Member Price | Non-Member Price |
---|---|---|
$17.00 | $21.00 |
Paper Abstract
In traditional unsupervised classification method, the number of clusters usually needs to be assigned subjectively by
analysts, but in fact, in most situations, the prior knowledge of the research subject is difficult to acquire, so the suitable
and best cluster numbers are very difficult to define. Therefore, in this research, an effective heuristic unsupervised
classification method-Genetic Algorithm (GA) is introduced and tested here, because it can be through the
mathematical model and calculating procedure of optimization to determine the best cluster numbers and centers
automatically. Furthermore, two well-known models--Davies-Bouldin's and the K-Means algorithm, which adopted by
most research for the applications in pattern classification, are integrated with GA as the fitness functions. In a word, in
this research, a heuristic method-Genetic Algorithm (GA), is adopted and integrated with two different indices as the
fitness functions to automatically interpret the clusters of satellite images for unsupervised classification. The
classification results were compared to conventional ISODATA results, and to ground truth information derived from a
topographic map for the estimation of classification accuracy. All image-processing program is developed in MATLAB,
and the GA unsupervised classifier is tested on several image examples.
Paper Details
Date Published: 29 September 2006
PDF: 12 pages
Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 63650U (29 September 2006); doi: 10.1117/12.689299
Published in SPIE Proceedings Vol. 6365:
Image and Signal Processing for Remote Sensing XII
Lorenzo Bruzzone, Editor(s)
PDF: 12 pages
Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 63650U (29 September 2006); doi: 10.1117/12.689299
Show Author Affiliations
T. Y. Tsai, National Chung Hsing Univ. (Taiwan)
Published in SPIE Proceedings Vol. 6365:
Image and Signal Processing for Remote Sensing XII
Lorenzo Bruzzone, Editor(s)
© SPIE. Terms of Use
