A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
A Knowledge-based Approach to Urban Feature Classification Using Aerial Imagery with Lidar Data
2008
Photogrammetric Engineering and Remote Sensing
While the spatial resolution of remotely sensed data has improved, multispectral imagery is still not sufficient for urban classification. Problems include the difficulty in discriminating between trees and grass, the misclassification of buildings due to diverse roof compositions and shadow effects, and the misclassification of cars on roads. Recently, lidar (light detection and ranging) data have been integrated with remotely sensed data to obtain better classification results. In this study,
doi:10.14358/pers.74.12.1473
fatcat:risqy4ib7zffvhfzi4pdxmv674