FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery
Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-taken photos. This paper critically evaluates...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4715 |
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| author | Safouane El Ghazouali Arnaud Gucciardi Francesca Venturini Nicola Venturi Michael Rueegsegger Umberto Michelucci |
| author_facet | Safouane El Ghazouali Arnaud Gucciardi Francesca Venturini Nicola Venturi Michael Rueegsegger Umberto Michelucci |
| author_sort | Safouane El Ghazouali |
| collection | DOAJ |
| description | Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-taken photos. This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. The goal is to enable researchers to choose efficiently from algorithms that are trainable and usable in real time on a deep learning infrastructure with moderate requirements. Using the large HRPlanesV2 dataset, together with rigorous validation with the GDIT dataset, this research encompasses an array of methodologies including YOLO versions 5, 8, and 10, Faster RCNN, CenterNet, RetinaNet, RTMDet, DETR, and grounding DINO, all trained from scratch. This exhaustive training and validation study reveals YOLOv5 as the pre-eminent model for the specific case of identifying airplanes from remote sensing data, showcasing high precision and adaptability across diverse imaging conditions. This research highlights the nuanced performance landscapes of these algorithms, with YOLOv5 emerging as a robust solution for aerial object detection, underlining its importance through superior mean average precision, recall, and intersection over union scores. The findings described here underscore the fundamental role of algorithm selection aligned with the specific demands of satellite imagery analysis and extend a comprehensive framework to evaluate model efficacy. This aims to foster exploration and innovation in the realm of remote sensing object detection, paving the way for improved satellite imagery applications. |
| format | Article |
| id | doaj-art-3bff3e49985541df95a849fcc8532e48 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-3bff3e49985541df95a849fcc8532e482024-12-27T14:50:58ZengMDPI AGRemote Sensing2072-42922024-12-011624471510.3390/rs16244715FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite ImagerySafouane El Ghazouali0Arnaud Gucciardi1Francesca Venturini2Nicola Venturi3Michael Rueegsegger4Umberto Michelucci5TOELT LLC, Research and Development, 8600 Dübendorf, SwitzerlandTOELT LLC, Research and Development, 8600 Dübendorf, SwitzerlandTOELT LLC, Research and Development, 8600 Dübendorf, SwitzerlandCompetence Center for Artificial Intelligence and Simulation, Armasuisse S+T, Feuerwerkerstrasse 39, 3602 Thun, SwitzerlandCompetence Center for Artificial Intelligence and Simulation, Armasuisse S+T, Feuerwerkerstrasse 39, 3602 Thun, SwitzerlandTOELT LLC, Research and Development, 8600 Dübendorf, SwitzerlandObject detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-taken photos. This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. The goal is to enable researchers to choose efficiently from algorithms that are trainable and usable in real time on a deep learning infrastructure with moderate requirements. Using the large HRPlanesV2 dataset, together with rigorous validation with the GDIT dataset, this research encompasses an array of methodologies including YOLO versions 5, 8, and 10, Faster RCNN, CenterNet, RetinaNet, RTMDet, DETR, and grounding DINO, all trained from scratch. This exhaustive training and validation study reveals YOLOv5 as the pre-eminent model for the specific case of identifying airplanes from remote sensing data, showcasing high precision and adaptability across diverse imaging conditions. This research highlights the nuanced performance landscapes of these algorithms, with YOLOv5 emerging as a robust solution for aerial object detection, underlining its importance through superior mean average precision, recall, and intersection over union scores. The findings described here underscore the fundamental role of algorithm selection aligned with the specific demands of satellite imagery analysis and extend a comprehensive framework to evaluate model efficacy. This aims to foster exploration and innovation in the realm of remote sensing object detection, paving the way for improved satellite imagery applications.https://www.mdpi.com/2072-4292/16/24/4715object detectionsurveyremote sensingsatellite imageaircraftlocalization |
| spellingShingle | Safouane El Ghazouali Arnaud Gucciardi Francesca Venturini Nicola Venturi Michael Rueegsegger Umberto Michelucci FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery Remote Sensing object detection survey remote sensing satellite image aircraft localization |
| title | FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery |
| title_full | FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery |
| title_fullStr | FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery |
| title_full_unstemmed | FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery |
| title_short | FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery |
| title_sort | flightscope an experimental comparative review of aircraft detection algorithms in satellite imagery |
| topic | object detection survey remote sensing satellite image aircraft localization |
| url | https://www.mdpi.com/2072-4292/16/24/4715 |
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