Image object detection is an important application of remote sensing technology. Road vehicle
detection using very high-resolution remote sensing images has a unique advantage of covering a large
area such as jaddah and alriad at the same time over all ground-based detectors. But the detection of small
vehicle-object in remote sensing imagery is still a challenging task. Several parameters of gray values and
sizes are examined to classify the objects in the image. The vehicles and their associated shadows can be
discriminated by removing big objects such as roads. Our test shows a promising result of detecting the
vehicle. We present an object-based detection approach with IMAGINE Objective for the detected vehicles
in the study area. (jeddah city , alriad city )
IMAGINE Objective employs feature models working on objects produced by image segmentation
and various other pixel-based algorithms can be processed by geometric and textural parameters after being
vectorised. This process is used and optimised for the extraction of the vehicles from high resolution images.
Preliminary object detection tests using a semi automated post-classification approach show reliable results.
For this research, the applied methods prove to be useful to detect vehicles on the road of example images
even without producing a complete extraction of all vehicles. The approach will be extended to the whole
images area.