Extracting Building Areas from Photogrammetric DSM and DOM by Automatically Selecting Training Samples from Historical DLG Data

Siyang Chen,Ke Nie,Xiaoming Li,Weixi Wang,Yunsheng Zhang

doi:10.3390/ijgi9010018

Abstract

This paper presents an automatic building extraction method which utilizes a photogrammetric digital surface model (DSM) and digital orthophoto map (DOM) with the help of historical digital line graphic (DLG) data. To reduce the need for manual labeling, the initial labels were automatically obtained from historical DLGs. Nonetheless, a proportion of these labels are incorrect due to changes (e.g., new constructions, demolished buildings). To select clean samples, an iterative method using random forest (RF) classifier was proposed in order to remove some possible incorrect labels. To get effective features, deep features extracted from normalized DSM (nDSM) and DOM using the pre-trained fully convolutional networks (FCN) were combined. To control the computation cost and alleviate the burden of redundancy, the principal component analysis (PCA) algorithm was applied to reduce the feature dimensions. Three data sets in two areas were employed with evaluation in two aspects. In these data sets, three DLGs with 15%, 65%, and 25% of noise were applied. The results demonstrate the proposed method could effectively select clean samples, and maintain acceptable quality of extracted results in both pixel-based and object-based evaluations.

Highlights

With the ongoing urbanization and city expansion worldwide, many international cities are experiencing rising construction activities
The clean samples can be selected by the proposed iterative method via random forest (RF) classifier considering unbalanced samples
By comparing results based on four different strategies in feature selection, the importance of deep features and the necessity of combing both height information and spectral information can be seen

Summary

Introduction

With the ongoing urbanization and city expansion worldwide, many international cities are experiencing rising construction activities. Many cities in China are expressing the need to construct smart cities, the intelligent understanding of geographical information from different sensors (e.g., remote sensed images, laser scanning point clouds) becomes a necessity for city management departments. Building extraction serves an important role and it is the basis for building change detection, three-dimensional (3D) building modeling, and further urban planning. Building extraction is a popular research topic in the field of photogrammetry and computer vision. Automatic building extraction methods have become a hot research topic for scholars worldwide. A great variety of methods have been proposed, and they can be generally classified into two categories: unsupervised methods and supervised methods

Results

Discussion

Conclusion