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

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

Computation · Computer vision · Data mining · Data redundancy · Database · Feature extraction · Orthophoto · Photogrammetry · Principal component analysis · 3D Surveying and Cultural Heritage · Computer Science · Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture · Artificial Intelligence

• Sky-Scanning for Energy

  Open Access•Yaofu Huang, Weipan Xu et al.•ISPRS International Journal of…•2024

  Add to list

• Fully convolutional networks for semantic segmentation

  Jonathan Long, Evan Shelhamer et al.•2015 IEEE Conference on Computer…•2015

  Add to list