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Journal of Sensors
Volume 2016, Article ID 1795205, 13 pages
http://dx.doi.org/10.1155/2016/1795205
Research Article

Individual Building Rooftop and Tree Crown Segmentation from High-Resolution Urban Aerial Optical Images

School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received 2 September 2015; Accepted 8 December 2015

Academic Editor: Chiman Kwan

Copyright © 2016 Jichao Jiao and Zhongliang Deng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We segment buildings and trees from aerial photographs by using superpixels, and we estimate the tree’s parameters by using a cost function proposed in this paper. A method based on image complexity is proposed to refine superpixels boundaries. In order to classify buildings from ground and classify trees from grass, the salient feature vectors that include colors, Features from Accelerated Segment Test (FAST) corners, and Gabor edges are extracted from refined superpixels. The vectors are used to train the classifier based on Naive Bayes classifier. The trained classifier is used to classify refined superpixels as object or nonobject. The properties of a tree, including its locations and radius, are estimated by minimizing the cost function. The shadow is used to calculate the tree height using sun angle and the time when the image was taken. Our segmentation algorithm is compared with other two state-of-the-art segmentation algorithms, and the tree parameters obtained in this paper are compared to the ground truth data. Experiments show that the proposed method can segment trees and buildings appropriately, yielding higher precision and better recall rates, and the tree parameters are in good agreement with the ground truth data.