Automatic Target Detection from Satellite Imagery Using Machine Learning

Article


Tahir, Arsalan, Munawar, Hafiz Suliman, Akram, Junaid, Adil, Muhammad, Ali, Shehryar, Kouzani, Abbas Z. and Mahmud, M. A. Parvez. 2022. "Automatic Target Detection from Satellite Imagery Using Machine Learning." Sensors. 22 (3), pp. 1-22. https://doi.org/10.3390/s22031147
Article Title

Automatic Target Detection from Satellite Imagery Using Machine Learning

ERA Journal ID34304
Article CategoryArticle
AuthorsTahir, Arsalan, Munawar, Hafiz Suliman, Akram, Junaid, Adil, Muhammad, Ali, Shehryar, Kouzani, Abbas Z. and Mahmud, M. A. Parvez
Journal TitleSensors
Journal Citation22 (3), pp. 1-22
Article Number1147
Number of Pages22
Year2022
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN1424-8220
1424-8239
Digital Object Identifier (DOI)https://doi.org/10.3390/s22031147
Web Address (URL)https://www.mdpi.com/1424-8220/22/3/1147
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 Digital Globe com-prises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). 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 an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

KeywordsDeep learning; Faster RCNN; Satellite images; SIMRDWN; SSD; YOLO
Byline AffiliationsNational University of Sciences and Technology, Pakistan
University of New South Wales
Superior University, Pakistan
University of Sydney
Deakin University
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