Abstract:
Object detection is a vital step in satellite imagery-based computer vision applications
such as precision agriculture, urban planning and defense applications. In satellite imagery,
object detection is a very complicated task due to various reasons including low
pixel resolution of objects and detection of small objects in the large scale (a single
satellite image taken by DigitalGlobe comprises over 240 million pixels) satellite images.
Object detection in satellite images has many challenges like class variations, multiple
object poses, high variance in object size, illumination and dense background. This
study aims to compare the performance of existing deep learning algorithms. We created
the dataset of satellite imagery to perform object detection using Convolutional
Neural Network based frameworks like Faster RCNN, YOLOv3, SSD and SIMRDWN.
In addition to that, we also performed an analysis of these approaches in terms of accuracy
and speed using the developed dataset of satellite imagery. The results showed
that SIMRDWN has good accuracy of 97% and speed on high-resolution images, while
Faster RCNN has good accuracy of 95.31% on the standard resolution (1000 × 600).
YOLOv3 has good speed and accuracy of 94.20% on standard resolution (416 × 416)
while on the other hand SSD has just good speed on standard resolution (300 × 300)
with accuracy of 84.61%.
