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

Jichao Jiao,Zhongliang Deng

doi:10.1155/2016/1795205

Jichao Jiao, Zhongliang Deng

Open Access

https://doi.org/10.1155/2016/1795205

Copy DOI

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.

Highlights

With the fast pace of industrialization and urbanization, 3D models are more and more necessary for urban planning, flight simulator, and military training
The method is implemented by two steps: (1) the first step is presegmentation, and the result is compared with the result which is got by Turbopixel and Entropy Rate Superpixel (ERS); (2) the second is to segment buildings and trees, and the segmentation result is compared to the result got by using Turbopixel and ERS [34]; (3) the tree’s parameters estimation result is evaluated by the root mean square error (RMSE)
We proposed a building and tree detection algorithm by using improved superpixels from large highresolution urban images, and we proposed a method to calculate the tree parameters depending upon a cost function and shadows

Summary

Introduction

With the fast pace of industrialization and urbanization, 3D models are more and more necessary for urban planning, flight simulator, and military training. It is important to identify buildings and trees in high-resolution aerial photographs for displacement maps in real-time, which is a key procedure for building 3D models, because buildings and trees are significant features for city modeling, and often occlude other elements in 3D urban models. The first step of displacement maps is to detect buildings and trees from aerial photographs. Automatic detection of trees and buildings is a challenging work because, first of all, the input data sets of aerial photographs are huge; second, the features of buildings and trees are various, and it is hard to find salient ones for training purposes; third, it is difficult to classify building from ground and classify tree from grass in desert climate regions like Arizona State in USA. We will provide a description of prior art of building and tree detection from three aspects: (1) buildings and trees segmentation; (2) superpixel refinement; (3) salient region feature

Objectives

Results

Discussion

Conclusion