TITLE:
Segment-Based Terrain Filtering Technique for Elevation-Based Building Detection in VHR Remote Sensing Images
AUTHORS:
Alaeldin Suliman, Yun Zhang
KEYWORDS:
Building Detection, Terrain Filtering, Elevation Normalization, VHR Imagery
JOURNAL NAME:
Advances in Remote Sensing,
Vol.5 No.3,
September
26,
2016
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.