Generation of fused visible and thermal-infrared images for uncooperative spacecraft proximity navigation

Civardi, Gaia Letizia; Bechini, Michele; Quirino, Matteo; Colombo, Alessandro; Piccinin, Margherita; Lavagna, Michèle

doi:10.1016/j.asr.2023.03.022

Cited by 4 publications

(6 citation statements)

References 39 publications

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“…For a detailed description of the code and the numerical testing, the reader can refer to [8,9]. The code has been compared to real thermal vacuum test data in a CubeSat scenario proving to be reliable in computing the temperature field; moreover, preliminary mono-material cases using such code for thermal image generation are presented in [2,10,11].…”

Section: Finite Volume Thermal Modelmentioning

confidence: 99%

“…The preliminary results of the tool presented in Section 2 were applied in [2,10,11] for image fusion with the goal to enhance optical image visibility using thermal infrared camera data. The context of such research work was focused on facing problems like the one presented in Figure 23c, where in a proximity scenario, the target object, in this case, the TANGO satellite, enters a low visibility condition such as the eclipse phase, where the object is in complete shadow.…”

Section: Image Fusionmentioning

confidence: 99%

“…Thermal cameras can guarantee the detectability of the shape of the asteroid in all conditions. Furthermore, the biggest advantage of thermal images over optical ones is that they enable the detection of objects in the deep sky more easily despite the lower instrument resolution [1,2]. Indeed, optical cameras have more pixels which translate into bigger spatial resolution and lower detection limits for the size of the objects, but optical images capture all the star lights with a resulting image full of bright spots other than the target.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, there is novel literature on the application of thermal infrared images for navigation in adverse illumination conditions in proximity scenarios, for example, for active debris removal [4][5][6]. In the same context, an interesting application of TIR images is their combination with visible (VIS) images using fusion algorithms to retain the advantages of both sensors (i.e., high definition for VIS and robustness in low illumination conditions for TIR); on this matter the interested reader can refer to [2,7,8].…”

Section: Introductionmentioning

confidence: 99%

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Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios

Quirino,

Lavagna

2024

Applied Sciences

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On-orbit autonomous relative navigation performance strongly depends on both sensor suite and state reconstruction selection. Whenever that suite relies on image-based sensors working in the visible spectral band, the illumination conditions strongly affect the accuracy and robustness of the state reconstruction outputs. To cope with that limitation, we investigate the effectiveness of exploiting image sensors active in the IR spectral band, not limited by the lighting conditions. To run effective and comprehensive testing and validation campaigns on navigation algorithms, a large dataset of images is required, either available or easy to obtain in the visible band, not trivial and not accessible for the thermal band. The paper presents an open-source tool that exploits accurate finite volume thermal models of celestial objects and artificial satellites to create thermal images based on the camera dynamic. The thermal model relies on open CFD code (OpenFOAM), pushed to catch the finest details of the terrain or of the target geometries, and then the temperature field is processed to compute the view factors between the camera and each face of the mesh; thus, the radiative flux emitted by each face is extracted. Such data feed the rendering engine (Blender) that, together with the camera position and attitude, outputs the thermal image. The complete pipeline, fed by the orbiting target and the imaging sensor kinematic, outputs a proper synthetic thermal image dataset, exploitable either by a relative navigation block or any other scope of research. Furthermore, in the same framework, the article proposes two different thermal sensor models but any sensor model can be applied, providing full customization of the output. The tool performance is critically discussed and applied for two typical proximity scenarios, asteroid and artificial satellite; for both cases, the challenges and capabilities of the implemented tool for synthetic thermal images are highlighted. In the end, the tool is applied in a phase B mission design sponsored by ESA and in related research works; for such cases, the results are reported in the article.

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Section: Finite Volume Thermal Modelmentioning

confidence: 99%

Section: Image Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios

Quirino,

Lavagna

2024

Applied Sciences

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“…However, infrared images complement the advantages and disadvantages of visible light images, obtaining a large amount of thermal radiation information, with lower texture and spatial resolution. The fusion of the two can improve the contrast, clarity, and details of the image, thereby improving the performance of target detection [12], tracking [13], pedestrian re-identification [14] and semantic segmentation [15], Earth observations and Spacecraft relative navigation [16].…”

Section: Introductionmentioning

confidence: 99%

BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

Wang,

Zhang,

Tao

et al. 2024

CMC

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While single-modal visible light images or infrared images provide limited information, infrared light captures significant thermal radiation data, whereas visible light excels in presenting detailed texture information. Combining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations, resulting in high-quality images with enhanced contrast and rich texture details. Such capabilities hold promising applications in advanced visual tasks including target detection, instance segmentation, military surveillance, pedestrian detection, among others. This paper introduces a novel approach, a dual-branch decomposition fusion network based on AutoEncoder (AE), which decomposes multi-modal features into intensity and texture information for enhanced fusion. Local contrast enhancement module (CEM) and texture detail enhancement module (DEM) are devised to process the decomposed images, followed by image fusion through the decoder. The proposed loss function ensures effective retention of key information from the source images of both modalities. Extensive comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture details. From the qualitative results, we can see the advantages of fusion details and local contrast. In the quantitative experiments, entropy (EN), mutual information (MI), structural similarity (SSIM) and other results have improved and exceeded the SOTA (State of the Art) model as a whole.

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Robust spacecraft relative pose estimation via CNN-aided line segments detection in monocular images

Bechini,

Gu,

Lunghi

et al. 2024

Acta Astronautica

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Generation of fused visible and thermal-infrared images for uncooperative spacecraft proximity navigation

Cited by 4 publications

References 39 publications

Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios

Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios

BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

Robust spacecraft relative pose estimation via CNN-aided line segments detection in monocular images

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