Aerial
photos and satellite images tell us about the land surface. It provides a
variety of information such as particularly human-built objects such as
buildings, roads and bridges and the location and characteristics of the
vegetation. With the exception of aerial photographs and satellite imagery, the
collection, evaluation, and updating of the required data with other data
collection methods is a time-consuming process and more costly. Data from
aerial photographs and satellite images have long been detected manually by
conventional methods and by operators. The automatically of these detections
increases the speed of the project process and contributes to the reduction of
the expenses spent. The projects carried out within the scope of the extraction
and classification of objects are mostly concentrated on buildings and roads.
Because roads and buildings; Due to the characteristic features such as having
sharp lines and easy determination of the geometric shape, the identification
of the detail lines in the objects is easier than determining the details of
other objects. Various object extraction and classification techniques are used
for image analysis with semi and fully automatic approach methods based on
image processing techniques. In this study, aerial photographs of a certain
area of Afyon Kocatepe University Ahmet Necdet Sezer Campus were obtained by
using unmanned aerial vehicles (UAV). The raw data obtained were evaluated and
the object-based classification approach was used to automatic detection and
classify the roads of the university in the digital environment.
Primary Language | English |
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Journal Section | Research Articles |
Authors | |
Publication Date | June 1, 2020 |
Published in Issue | Year 2020 Volume: 2 Issue: 1 |