Skip to main content

2024 | OriginalPaper | Buchkapitel

Research on the Fusion Algorithm of Drone Images and Satellite Imagery

verfasst von : Xinwei Dong, Guowei Che, Chao Sun, Ruotong Zou, Lezhou Feng, Xiaoming Ding

Erschienen in: Communications, Signal Processing, and Systems

Verlag: Springer Nature Singapore

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

With the rapid development of drone technology, drone imagery has become an important means of obtaining high-resolution surface information. However, due to the operational height and range limitations of drones, there are issues of limited coverage and small data volume in drone imagery. Meanwhile, satellite imagery offers extensive coverage and a large amount of data but with lower resolution. In order to fully utilize the advantages of drone imagery and satellite imagery, and improve the accuracy of surface information extraction and spatial resolution, researchers have conducted studies on the fusion algorithms of drone imagery and satellite imagery. This article provides a review and analysis of the fusion algorithms for drone imagery and satellite imagery. Firstly, the characteristics and advantages of drone imagery and satellite imagery are introduced, emphasizing the importance of integrating the two. Furthermore, data loading and preprocessing techniques are discussed. Then, common fusion methods for drone imagery and satellite imagery are detailed, including pixel-level fusion, feature-level fusion, and decision-level fusion, among others. The evaluation methods for fusion quality are also explained. Finally, research achievements from both domestic and international sources are presented.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Beigi P, Rajabi MS, Aghakhani S (2022) An overview of drone energy consumption factors and models. arXiv preprint arXiv:2206.10775 Beigi P, Rajabi MS, Aghakhani S (2022) An overview of drone energy consumption factors and models. arXiv preprint arXiv:​2206.​10775
2.
Zurück zum Zitat Khalaf OI, Romero CAT, Hassan S et al (2022) Mitigating hotspot issues in heterogeneous wireless sensor networks. J Sens 2022:1–14CrossRef Khalaf OI, Romero CAT, Hassan S et al (2022) Mitigating hotspot issues in heterogeneous wireless sensor networks. J Sens 2022:1–14CrossRef
3.
Zurück zum Zitat Michałowska K, Głowienka E (2022) Multi-temporal analysis of changes of the southern part of the Baltic sea coast using aerial remote sensing data. Remote Sens 14(5):1212CrossRef Michałowska K, Głowienka E (2022) Multi-temporal analysis of changes of the southern part of the Baltic sea coast using aerial remote sensing data. Remote Sens 14(5):1212CrossRef
4.
Zurück zum Zitat Chen T, Song C, Zhan P et al (2022) Remote sensing estimation of the flood storage capacity of basin-scale lakes and reservoirs at high spatial and temporal resolutions. Sci Total Environ 807:150772CrossRef Chen T, Song C, Zhan P et al (2022) Remote sensing estimation of the flood storage capacity of basin-scale lakes and reservoirs at high spatial and temporal resolutions. Sci Total Environ 807:150772CrossRef
5.
Zurück zum Zitat Zhu Q, Guo X, Deng W et al (2022) Land-use/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery. ISPRS J Photogramm Remote Sens 184:63–78CrossRef Zhu Q, Guo X, Deng W et al (2022) Land-use/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery. ISPRS J Photogramm Remote Sens 184:63–78CrossRef
6.
Zurück zum Zitat Yang D, Guo J, Sun S et al (2022) An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting. Appl Energy 306:117992CrossRef Yang D, Guo J, Sun S et al (2022) An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting. Appl Energy 306:117992CrossRef
7.
Zurück zum Zitat Xiao Z, Gang W, Yuan J et al (2022) Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning. Energy Build 258:111832CrossRef Xiao Z, Gang W, Yuan J et al (2022) Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning. Energy Build 258:111832CrossRef
8.
Zurück zum Zitat Alseelawi N, Hazim HT, Salim ALRikabi HTH (2022) A novel method of multimodal medical image fusion based on hybrid approach of NSCT and DTCWT. Int J Online Biomed Eng 18(3) Alseelawi N, Hazim HT, Salim ALRikabi HTH (2022) A novel method of multimodal medical image fusion based on hybrid approach of NSCT and DTCWT. Int J Online Biomed Eng 18(3)
9.
Zurück zum Zitat Chen S, Chen J, Rao Y et al (2022) A hierarchical consensus attention network for feature matching of remote sensing images. IEEE Trans Geosci Remote Sens 60:1–11 Chen S, Chen J, Rao Y et al (2022) A hierarchical consensus attention network for feature matching of remote sensing images. IEEE Trans Geosci Remote Sens 60:1–11
10.
Zurück zum Zitat Azam MA, Khan KB, Salahuddin S et al (2022) A review on multimodal medical image fusion: compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. Comput Biol Med 144:105253CrossRef Azam MA, Khan KB, Salahuddin S et al (2022) A review on multimodal medical image fusion: compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. Comput Biol Med 144:105253CrossRef
11.
Zurück zum Zitat Jose J, Gautam N, Tiwari M et al (2021) An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomed Signal Process Control 66:102480CrossRef Jose J, Gautam N, Tiwari M et al (2021) An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomed Signal Process Control 66:102480CrossRef
12.
Zurück zum Zitat Javan FD, Samadzadegan F, Mehravar S et al (2021) A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery. ISPRS J Photogramm Remote Sens 171:101–117CrossRef Javan FD, Samadzadegan F, Mehravar S et al (2021) A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery. ISPRS J Photogramm Remote Sens 171:101–117CrossRef
13.
Zurück zum Zitat Ding K, Ma K, Wang S et al (2021) Comparison of full-reference image quality models for optimization of image processing systems. Int J Comput Vis 129:1258–1281CrossRef Ding K, Ma K, Wang S et al (2021) Comparison of full-reference image quality models for optimization of image processing systems. Int J Comput Vis 129:1258–1281CrossRef
14.
Zurück zum Zitat Liu J, Shang J, Liu R et al (2022) Attention-guided global-local adversarial learning for detail-preserving multi-exposure image fusion. IEEE Trans Circuits Syst Video Technol 32(8):5026–5040CrossRef Liu J, Shang J, Liu R et al (2022) Attention-guided global-local adversarial learning for detail-preserving multi-exposure image fusion. IEEE Trans Circuits Syst Video Technol 32(8):5026–5040CrossRef
15.
Zurück zum Zitat Wu S, An Y, Liu S et al (2015) Research and analysis of image fusion of UAV high resolution data and Landsat-8 multispectral data. J Guizhou Normal Univ (from Nat Sci Ed) 33(1):13–17 Wu S, An Y, Liu S et al (2015) Research and analysis of image fusion of UAV high resolution data and Landsat-8 multispectral data. J Guizhou Normal Univ (from Nat Sci Ed) 33(1):13–17
16.
Zurück zum Zitat Chen L, Liao A (2008) Comparison and analysis of fusion of aerial photo digitized image and SPOT5 multispectral image. Geogr Inf World 6(3):21–25 Chen L, Liao A (2008) Comparison and analysis of fusion of aerial photo digitized image and SPOT5 multispectral image. Geogr Inf World 6(3):21–25
17.
Zurück zum Zitat Jia Y, Sun J (1997) Research on the fusion method of remote sensing multispectral image data and aerial photo digital image. Surveying Mapp Bull (5):10–12 Jia Y, Sun J (1997) Research on the fusion method of remote sensing multispectral image data and aerial photo digital image. Surveying Mapp Bull (5):10–12
18.
Zurück zum Zitat Niu L, Li Y, Yang S et al (2019) Fusion algorithm for UAV aerial photo and satellite imagery. Remote Sens Inf 34(04):74–78 Niu L, Li Y, Yang S et al (2019) Fusion algorithm for UAV aerial photo and satellite imagery. Remote Sens Inf 34(04):74–78
19.
Zurück zum Zitat Masi G, Cozzolino D, Verdoliva L et al (2016) Pansharpening by convolutional neural networks. Remote Sens 8(7):594CrossRef Masi G, Cozzolino D, Verdoliva L et al (2016) Pansharpening by convolutional neural networks. Remote Sens 8(7):594CrossRef
20.
Zurück zum Zitat Zhong J, Yang B, Huang G et al (2016) Remote sensing image fusion with convolutional neural network. Sens Imaging 17(1):10 Zhong J, Yang B, Huang G et al (2016) Remote sensing image fusion with convolutional neural network. Sens Imaging 17(1):10
21.
Zurück zum Zitat Li H, Liu F, Yang S et al (2016) Learning networks based on deep support values remote sensing image fusion of remote sensing. J Comput Sci 39(8):1583–1596 Li H, Liu F, Yang S et al (2016) Learning networks based on deep support values remote sensing image fusion of remote sensing. J Comput Sci 39(8):1583–1596
22.
Zurück zum Zitat Jiang C, Zhang H, Shen H et al (2014) Two-step sparse coding for the pan-sharpening of remote sensing images. IEEE J Sel Top Appl Earth Observations Remote Sens 7(5):1792–1805 Jiang C, Zhang H, Shen H et al (2014) Two-step sparse coding for the pan-sharpening of remote sensing images. IEEE J Sel Top Appl Earth Observations Remote Sens 7(5):1792–1805
23.
Zurück zum Zitat Chen Y, Sun K, Yin J et al (2017) GF-2 image fusion method quality evaluation. Surveying Mapp Sci (11):1–10 Chen Y, Sun K, Yin J et al (2017) GF-2 image fusion method quality evaluation. Surveying Mapp Sci (11):1–10
25.
Zurück zum Zitat Li Y, Yan W, An S et al (2023) A spatio-temporal fusion framework of UAV and satellite imagery for winter wheat growth monitoring. Drones 7(1):23CrossRef Li Y, Yan W, An S et al (2023) A spatio-temporal fusion framework of UAV and satellite imagery for winter wheat growth monitoring. Drones 7(1):23CrossRef
26.
Zurück zum Zitat Zhao F, Wu X, Wang S (2020) Object-oriented vegetation classification method based on UAV and satellite image fusion. Procedia Comput Sci 174:609–615CrossRef Zhao F, Wu X, Wang S (2020) Object-oriented vegetation classification method based on UAV and satellite image fusion. Procedia Comput Sci 174:609–615CrossRef
27.
Zurück zum Zitat Li Z, Li E, Samat A et al (2022) An object-oriented CNN model based on improved superpixel segmentation for high-resolution remote sensing image classification. IEEE J Sel Top Appl Earth Observations Remote Sens 15:4782–4796CrossRef Li Z, Li E, Samat A et al (2022) An object-oriented CNN model based on improved superpixel segmentation for high-resolution remote sensing image classification. IEEE J Sel Top Appl Earth Observations Remote Sens 15:4782–4796CrossRef
28.
Zurück zum Zitat Zhang Y (2008) Methods for image fusion quality assessment-a review, comparison and analysis. Int Arch Photogramm Remote Sens Spat Inf Sci 37(PART B7):1101–1109 Zhang Y (2008) Methods for image fusion quality assessment-a review, comparison and analysis. Int Arch Photogramm Remote Sens Spat Inf Sci 37(PART B7):1101–1109
29.
Zurück zum Zitat Shi W, Zhu CQ, Tian Y et al (2005) Wavelet-based image fusion and quality assessment. Int J Appl Earth Obs Geoinf 6(3–4):241–251 Shi W, Zhu CQ, Tian Y et al (2005) Wavelet-based image fusion and quality assessment. Int J Appl Earth Obs Geoinf 6(3–4):241–251
30.
Zurück zum Zitat Bampis CG, Li Z, Bovik AC (2018) Spatiotemporal feature integration and model fusion for full reference video quality assessment. IEEE Trans Circuits Syst Video Technol 29(8):2256–2270CrossRef Bampis CG, Li Z, Bovik AC (2018) Spatiotemporal feature integration and model fusion for full reference video quality assessment. IEEE Trans Circuits Syst Video Technol 29(8):2256–2270CrossRef
Metadaten
Titel
Research on the Fusion Algorithm of Drone Images and Satellite Imagery
verfasst von
Xinwei Dong
Guowei Che
Chao Sun
Ruotong Zou
Lezhou Feng
Xiaoming Ding
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-7502-0_56

Neuer Inhalt