Segment-Based Terrain Filtering Technique for Elevation-Based Building Detection in VHR Remote Sensing Images ()
ABSTRACT
Building detection in very high resolution (VHR) remote sensing images is crucial for many urban
planning and management applications. Since buildings are elevated objects, the incorporation of
elevation data provides a mean to reliable detection. However, almost all existing methods of elevation-based building detection must first generate a normalized Digital Surface Model (nDSM).
This model is generated by processes of extracting and subtracting terrain elevations from the
DSM data. The generation of accurate nDSM is still a challenging task to some extent. This paper
introduces a segment-based terrain filtering (SegTF) technique to filter out the terrain elevations
directly using DSM elevations. This technique has four steps: elevation co-registration, image segmentation,
slope calculation, and building detection. These steps of the developed technique were
applied to a dataset that consisted of a VHR image and a corresponding DSM for detecting buildings.
The result of the building detection was evaluated and found to be 100% correct with an
overall detection quality of 93%. These values indicate a highly reliable and promising technique
for mapping buildings in VHR images.
Share and Cite:
Suliman, A. and Zhang, Y. (2016) Segment-Based Terrain Filtering Technique for Elevation-Based Building Detection in VHR Remote Sensing Images.
Advances in Remote Sensing,
5, 192-202. doi:
10.4236/ars.2016.53016.