제어로봇시스템학회:학술대회논문집
- 2001.10a
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- Pages.23.4-23
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- 2001
Unsupervised segmentation of Multi -Source Remotely Sensed images using Binary Decision Trees and Canonical Transform
- Mohammad, Rahmati (Amir Kabir University of Technology) ;
- Kim, Jung-Ha (Kookmin Univ.)
- Published : 2001.10.01
Abstract
This paper proposes a new approach to unsupervised classification of remotely sensed images. Fusion of optic images (Landsat TM) and radar data (SAR) has beer used to increase the accuracy of classification. Number of clusters is estimated using generalized Dunns measure. Performance of the proposed method is best observed comparing the classified images with classified aerial images.
Keywords