Next Article in Journal
Uncertainty Visualization of Transport Variance in a Time-Varying Ensemble Vector Field
Next Article in Special Issue
Identification of Salt Deposits on Seismic Images Using Deep Learning Method for Semantic Segmentation
Previous Article in Journal
Automatic Geodata Processing Methods for Real-World City Visualizations in Cities: Skylines
Previous Article in Special Issue
WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning
Open AccessArticle

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

1
School of Geoscience and Info-Physics, Central South University, Changsha 410083, China
2
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China
3
Research Institute of Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 516080, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(1), 18; https://doi.org/10.3390/ijgi9010018
Received: 14 November 2019 / Revised: 12 December 2019 / Accepted: 17 December 2019 / Published: 1 January 2020
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
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. View Full-Text
Keywords: building extraction; fully connected networks; photogrammetric DOM and DSM; historical DLG; dimension reduction building extraction; fully connected networks; photogrammetric DOM and DSM; historical DLG; dimension reduction
Show Figures

Figure 1

MDPI and ACS Style

Chen, S.; Zhang, Y.; Nie, K.; Li, X.; Wang, W. Extracting Building Areas from Photogrammetric DSM and DOM by Automatically Selecting Training Samples from Historical DLG Data. ISPRS Int. J. Geo-Inf. 2020, 9, 18.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop