CGA: A New Approach to Estimate the Geolocation of a Ground Target from Drone Aerial Imagery

Habchi, Ali El; Moumen, Younes; Zerrouk, Ilham; Khiati, Wassim; Berrich, Jamal; Bouchentouf, Toumi

doi:10.1109/icds50568.2020.9268749

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…The accuracy of DEM model is the core of this algorithm. There are mainly three ways to obtain DEM data: (1) Measuring on the ground by a hand-held GPS measuring instrument or total station; (2) Obtaining remote sensing images through photoelectric equipment and remote sensing technology, while carrying out three-dimensional reconstruction; (3) Using high-precision equipment such as radar and laser scanner to obtain elevation information like the 3D point cloud [65][66][67][68][69][70][71]. The accuracy of DEM model is the core of this algorithm.…”

Section: Target Location Algorithm Based On Digital Elevation Modelmentioning

confidence: 99%

“…Wang [50] analyzed the imaging link of the aerial camera for photogrammetry of the target and established a multifactor positioning error analysis model. Furthermore, scholars have established corresponding mathematical models or correction algorithms for different error factors [63][64][65][66][67][68][69][70][71][72][73]. Taking the height error as an example, Qiao [63,64] has established the target positioning algorithm based on the digital elevation model, which solved the problem of excessive target height error caused by the positioning algorithm based on the earth ellipsoid model; HAN [72] used the feature points in the image to obtain height information and locate the target without terrain data, but this method needs to control the imaging distance and the identifiable feature points must be ensured to exist in the graph.…”

Section: Introductionmentioning

confidence: 99%

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Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

et al. 2022

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Aerial cameras are one of the main devices for obtaining ground images in the air. Since the industrial community sets higher requirements of aerial cameras’ self-locating performance yearly using aerial cameras to locate ground targets has become a research hotspot in recent years. Based on the situation that no ground control point exists in target areas, the calculation principle of the aerial remote sensing image positioning algorithm has been analyzed by establishing different positioning models. Several error analysis models of the positioning algorithm based on the total differential method and the Monte Carlo method are established, and relevant factors that cause the positioning error are summarized. The last section proposes the optimization direction of aerial camera positioning algorithms in the future, which are verified by related simulation experiments. This paper provides a certain degree of guidelines in this area for researchers, who can quickly understand the current development and optimization direction of target geo-location algorithms of aerial remote sensing imagery.

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Section: Target Location Algorithm Based On Digital Elevation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

et al. 2022

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“…General-purpose object georeferencing has been studied primarily for airborne applications [ 22 , 23 , 24 ] and autonomous driving [ 25 ]. In the field of ship georeferencing, existing works made use of radar [ 26 ], remote sensing using synthetic aperture radar [ 27 ] and AIS [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Ship Segmentation and Georeferencing from Static Oblique View Images

Carrillo-Perez¹,

Barnes²,

Stephan³

2022

Sensors

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Camera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable and visualisable format. It therefore becomes important to have real-time recognition of ships present at the infrastructure, with their class and geographic position presented to the maritime situational awareness operator. This work presents a novel dataset, ShipSG, for the segmentation and georeferencing of ships in maritime monitoring scenes with a static oblique view. Moreover, an exploration of four instance segmentation methods, with a focus on robust (Mask-RCNN, DetectoRS) and real-time performances (YOLACT, Centermask-Lite) and their generalisation to other existing maritime datasets, is shown. Lastly, a method for georeferencing ship masks is proposed. This includes an automatic calculation of the pixel of the segmented ship to be georeferenced and the use of a homography to transform this pixel to geographic coordinates. DetectoRS provided the highest ship segmentation mAP of 0.747. The fastest segmentation method was Centermask-Lite, with 40.96 FPS. The accuracy of our georeferencing method was (22±10) m for ships detected within a 400 m range, and (53±24) m for ships over 400 m away from the camera.

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“…This support allows for the production of orthophotos, which are geometrically corrected aerial images, free from distortions (CHEN et al, 2023). Moreover, the sensor-rich environment of drones provides an abundance of data, facilitating precise estimation of the position of objects on the ground (HABCHI et al, 2020). This wealth of information enables farmers to make informed decisions, manage resources efficiently, and enhance crop yields through data-driven insights.…”

Section: Drones For Precision Farmingmentioning

confidence: 99%

“…Drone imagery plays a pivotal role in precision agriculture, supporting applications like mapping (RACHMAWATI et al, 2021) and geolocation estimation (HABCHI et al, 2020). The use of orthophotos and mosaics is widespread due to their capacity to provide undistorted, georeferenced images that serve as the foundation for a variety of applications.…”

Section: Mapping and Geolocation Estimation With Drone Imagerymentioning

confidence: 99%

Techniques for cattle detection, duplicate removal and counting in large pasture areas using multiple aerial images

Soares

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Agradeço primeiramente a Deus pelo dom da vida e Sua infinita misericórdia para comigo.Às instituições financeiras que apoiaram este projeto e o tornaram possível. Este trabalho foi realizado com o apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Código de Financiamento 001. Também agradeço à empresa Nitryx, que apoiou o projeto financeiramente, forneceu equipamentos e acesso às fazendas que serviram de base para a coleta dos dados necessários ao desenvolvimento deste trabalho.Ao meu orientador, Dr. Ricardo Campello, pela paciência, suporte intelectual e imensurável contribuição para o meu crescimento pessoal e profissional, desde o mestrado.Ao meu co-orientador, Dr. Moacir Ponti, por todo o conhecimento compartilhado e pela disposição e suporte ao assumir a co-orientação deste trabalho.Ao Dr. Rodrigo Gonçalves, que desempenhou um papel fundamental no surgimento e motivação para o desenvolvimento do tema proposto.Aos meus pais, José Luiz e Marli, que desde cedo me motivaram a estudar e aprender mais sobre computadores, o que se tornou uma paixão.Ao meu irmão, Vinícius, pela compreensão e apoio em minha vida acadêmica, muitas vezes tendo que cobrir minha ausência no trabalho devido à universidade. Agradeço à minha esposa, Dominique Ferreira, minha companheira que sempre me suporta em amor. Sem ela, eu não teria ingressado no ensino superior e, consequentemente, não teria chegado até aqui.Por fim, agradeço ao meu filho, Elliot Ferreira Soares, que nasceu durante este doutorado e, desde então, tem sido minha constante fonte de motivação para ser sempre alguém melhor."As coisas são mais belas quando vistas de cima."

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CGA: A New Approach to Estimate the Geolocation of a Ground Target from Drone Aerial Imagery

Cited by 8 publications

References 8 publications

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

Ship Segmentation and Georeferencing from Static Oblique View Images

Techniques for cattle detection, duplicate removal and counting in large pasture areas using multiple aerial images

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