Paper Info
Title
Individual Building Rooftop and Tree Crown Segmentation from High-Resolution Urban Aerial Optical Images.
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.
Year
DOI
Venue
2016
10.1155/2016/1795205
JOURNAL OF SENSORS
Field
DocType
Volume
Computer vision,Shadow,Feature vector,Naive Bayes classifier,Pattern recognition,Segmentation,Ground truth,Image complexity,Artificial intelligence,Engineering,Classifier (linguistics),Salient
Journal
2016
ISSN
Citations 
PageRank 
1687-725X
0
0.34
References 
Authors
8
2
Name
Order
Citations
PageRank
Jichao Jiao1186.53
Zhongliang Deng2217.47