A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Classification of quickbird image with maximal mutual information feature selection and support vector machine
2009
Procedia Earth and Planetary Science
This paper presents a method to select optimal feature subset from object-orientated image segmentation according to the maximal mutual information to improve classification accuracy of high spatial resolution imagery over urban area. The proposed method is a three-step classification routine that involves the integration of 1) image segmentation with eCoginition software, 2) feature selection by maximal mutual information criterion, and 3) support vector machine for classification. Experiment
doi:10.1016/j.proeps.2009.09.179
fatcat:aks44ykj2nddpi4lkjtgmieg3q