Automatic Target Detection from Satellite Imagery Using Machine Learning
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 dete...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/3/1147 |
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| author | Arsalan Tahir Hafiz Suliman Munawar Junaid Akram Muhammad Adil Shehryar Ali Abbas Z. Kouzani M. A. Parvez Mahmud |
| author_facet | Arsalan Tahir Hafiz Suliman Munawar Junaid Akram Muhammad Adil Shehryar Ali Abbas Z. Kouzani M. A. Parvez Mahmud |
| author_sort | Arsalan Tahir |
| collection | DOAJ |
| description | 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 comprises 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. |
| first_indexed | 2024-03-09T23:07:15Z |
| format | Article |
| id | doaj.art-d3827c09d4f84ad3ac6bb2f028b31893 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:07:15Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d3827c09d4f84ad3ac6bb2f028b318932023-11-23T17:51:15ZengMDPI AGSensors1424-82202022-02-01223114710.3390/s22031147Automatic Target Detection from Satellite Imagery Using Machine LearningArsalan Tahir0Hafiz Suliman Munawar1Junaid Akram2Muhammad Adil3Shehryar Ali4Abbas Z. Kouzani5M. A. Parvez Mahmud6Research Center for Modeling and Simulation, National University of Sciences and Technology, Islamabad 64000, PakistanSchool of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, AustraliaDepartment of Computer Science, Superior University, Lahore 54700, PakistanResearch Center for Modeling and Simulation, National University of Sciences and Technology, Islamabad 64000, PakistanSchool of Engineering, Deakin University, Geelong, VIC 3216, AustraliaSchool of Engineering, Deakin University, Geelong, VIC 3216, AustraliaSchool of Engineering, Deakin University, Geelong, VIC 3216, AustraliaObject 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 comprises 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.https://www.mdpi.com/1424-8220/22/3/1147deep learningsatellite imagesYOLOfaster RCNNSSDSIMRDWN |
| spellingShingle | Arsalan Tahir Hafiz Suliman Munawar Junaid Akram Muhammad Adil Shehryar Ali Abbas Z. Kouzani M. A. Parvez Mahmud Automatic Target Detection from Satellite Imagery Using Machine Learning Sensors deep learning satellite images YOLO faster RCNN SSD SIMRDWN |
| title | Automatic Target Detection from Satellite Imagery Using Machine Learning |
| title_full | Automatic Target Detection from Satellite Imagery Using Machine Learning |
| title_fullStr | Automatic Target Detection from Satellite Imagery Using Machine Learning |
| title_full_unstemmed | Automatic Target Detection from Satellite Imagery Using Machine Learning |
| title_short | Automatic Target Detection from Satellite Imagery Using Machine Learning |
| title_sort | automatic target detection from satellite imagery using machine learning |
| topic | deep learning satellite images YOLO faster RCNN SSD SIMRDWN |
| url | https://www.mdpi.com/1424-8220/22/3/1147 |
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