terrain extraction in built-up areas from satellite stereo-imagery-derived surface models: a stratified object-based approach

Clicks: 374
ID: 243422
2017
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #2 of 94 articles by views in población y desarrollo

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Very high spatial resolution (VHSR) stereo-imagery-derived digital surface models (DSM) can be used to generate digital elevation models (DEM). Filtering algorithms and triangular irregular network (TIN) densification are the most common approaches. Most filter-based techniques focus on image-smoothing. We propose a new approach which makes use of integrated object-based image analysis (OBIA) techniques. An initial land cover classification is followed by stratified land cover ground point sample detection, using object-specific features to enhance the sampling quality. The detected ground point samples serve as the basis for the interpolation of the DEM. A regional uncertainty index (RUI) is calculated to express the quality of the generated DEM in regard to the DSM, based on the number of samples per land cover object. The results of our approach are compared to a high resolution Light Detection and Ranging (LiDAR)-DEM, and a high level of agreement is observed—especially for non-vegetated and scarcely-vegetated areas. Results show that the accuracy of the DEM is highly dependent on the quality of the initial DSM and—in accordance with the RUI—differs between the different land cover classes.
Reference Key
luethje2017isprsterrain Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Fritjof Luethje;Dirk Tiede;Clemens Eisank
Journal población y desarrollo
Year 2017
DOI
10.3390/ijgi6010009
URL
http://www.mdpi.com/2220-9964/6/1/9
Keywords
urban automation transformation digital surface model built-upgeography (general)

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.