Detect Residential Buildings from Lidar and Aerial Photographs through Object-Oriented Land-Use Classification
Relating less directly to the physical reflectance from remote sensors, land-use analysis is comparably more challenging than land-cover studies, especially for residential land-uses. This research presents an object-oriented approach to detect residential land use of buildings directly
from lidar data, aerial photography, and road maps to enhance urban land-use analysis. Specifically, the proposed methodology applies a multi-directional ground filter to generate a bare ground surface from lidar data, then uses a morphology-based building detection algorithm to identify buildings
from lidar and aerial photographs, and finally separates residential buildings using a supervised C4.5 decision tree analysis based on seven land-use characteristics of buildings. Experiments based on the 8.25 km2 study site located in Austin, Texas proved the possibility and efficiency
of directly detecting and identifying residential buildings from remote sensing images with 81.1 percent of residential buildings correctly classified.
Document Type: Research Article
Publication date: 01 January 2012
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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