Skip to main content
SpringerLink
Log in

Feature extraction and analysis of landscape imaging using drones and machine vision

  • Data analytics and machine learning
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The development of drone and computer vision technologies has enabled automated landscape image analysis, unlocking new feature extraction capabilities. This paper presents an integrated framework leveraging aerial drone data and machine learning for landscape imaging. A diverse dataset of drone-captured scenery spanning urban and rural areas provides the foundation. Preprocessing techniques ready the images before quantitative color analysis and texture feature extraction reveal topographical patterns. Structural boundaries are identified through edge detection and Hough transforms. Deep convolutional neural networks semantically segment images into classified landscape regions. Weighted color block matching retrieves similar images by prioritizing salient areas. Experiments demonstrate 95% accuracy in landscape feature classification, with precision of 0.93, recall of 0.95, and F1 score of 0.94, outperforming existing methods. Extracted color, texture, spectral, and geometric patterns enable interpretation of terrain, vegetation health, human-made objects, and ecological processes. The proposed approach facilitates monitoring, decision-making, and optimization across agriculture, urban planning, conservation, and other applications. Overall, the results validate that combining drones, computer vision, and deep learning can automate the analysis of complex landscape images to generate actionable insights. This methodology pioneers the next generation of intelligent remote sensing systems for comprehending our living environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

References

  • Abkar A, Rastiveis H, Samadzadegan F, Reinartz P (2019) Multi-level feature fusion in a convolutional neural network for semantic segmentation in VHR aerial images. Remote Sensing 11(11):1372

    Google Scholar 

  • Ali, M., Yin, B., Kumar, A., Sheikh, A.M. et al. 2020, July. Reduction of Multiplications in Convolutional Neural Networks. In 2020 39th Chinese Control Conference (CCC). IEEE. p. 7406–7411. Doi: https://doi.org/10.23919/CCC50068.2020.9188843.

  • Aslam XD, Hou J, Li Q, Ullah R, Ni Z, Liu Y (2020) Reliable control design for composite-driven scheme based on delay networked T-S fuzzy system. Int J Robust Nonlinear Control. 30(4):1622–1642

    Article  MathSciNet  MATH  Google Scholar 

  • Azimi SM, Vig E, Kurz C (2019) Mapping crop types in Southeast Asia using Sentinel-1A backscatter and landsat-derived vegetation indices. Remote Sensing 11(11):1382

    Google Scholar 

  • Campa G, Napolitano MR, Fravolini ML (2009) Simulation environment for machine vision-based aerial refueling for UAVs. IEEE Trans Aerosp Electron Syst 45(1):138–151

    Article  Google Scholar 

  • Chen Z (2019) Observer-based dissipative output feedback control for network T-S fuzzy systems under time delays with mismatch premise. Nonlinear Dyn 95:2923–2941

    Article  MATH  Google Scholar 

  • Cheng G, Han J (2020) A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 157:11–28

    Google Scholar 

  • Cheng G, Han J, Lu X, Wang Y (2019) Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks. IEEE J Sel Topics Appl Earth Obs Remote Sens 13:3735–3756

    Article  Google Scholar 

  • Cheng G, Zhou P, Han J (2020) Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans Geosci Remote Sens 54(12):7405–7415

    Article  Google Scholar 

  • Dandois JP, Olano M, Ellis EC (2015) Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sensing 7(10):13895–13920

    Article  Google Scholar 

  • Dhawale S, Magán Á, Vagliviello T, Montesinos P, Mitchell E (2019) Detection of citrus canker using hyperspectral reflectance imaging and machine learning classifiers. Remote Sens 11(16):1883

    Google Scholar 

  • Ding L, Bruzzone L, Mattioli V, Du P (2019) Automatic Road extraction in remote sensing imagery by information fusion of lidar/InSAR data using structured learning. IEEE Trans Geosci Remote Sens 57(5):2669–2687

    Google Scholar 

  • Ding J, Chen B, Liu H, Huang M (2020) Convolutional neural network with data augmentation for SAR target recognition. IEEE Geosci Remote Sens Lett 17(3):491–495

    Google Scholar 

  • Donmez C, Villi O, Berberoglu S, Cilek A (2021) Computer vision-based citrus tree detection in a cultivated environment using UAV imagery. Comput Electron Agric 187:106273

    Article  Google Scholar 

  • Dou H, Liu Y, Chen S et al (2023) A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways. Soft Comput 27:16373–16388. https://doi.org/10.1007/s00500-023-09164-y

    Article  Google Scholar 

  • Duarte D, Nex F, Kerle N, Vosselman G (2018) Satellite image classification of building damages using airborne and satellite image samples in a deep learning approach. ISPRS Ann Photogramm, Remote Sens Spatial Inf Sci. 4(2):89

    Article  Google Scholar 

  • Ghamisi P, Rasti B, Yokoya N, Wang Q, Hofle B, Bruzzone L, Bovolo F, Chi M, Anders K, Gloaguen R (2020) Multisource and multitemporal data fusion in remote sensing: a comprehensive review of the state of the art. IEEE Geosci Remote Sens Mag 8(1):6–39

    Article  Google Scholar 

  • Goodarzi P, Ansari M, Rahimian FP, Mahdavinejad M, Park C (2023) Incorporating sparse model machine learning in designing cultural heritage landscapes. Autom Constr 155:105058

    Article  Google Scholar 

  • Hamylton SM, Hedley JD, Beaman RJ (2018) Derivation of high-resolution benthic terrain models for Great Barrier Reef environments from airborne lidar and optical remote sensing. Remote Sens Environ 217:276–293

    Google Scholar 

  • Hartling S, Sagan V, Maimaitijiang M (2021) Urban tree species classification using UAV-based multi-sensor data fusion and machine learning. Gisci Remote Sens 58(8):1250–1275

    Article  Google Scholar 

  • Hong D, Yokoya N, Chanussot J, Zhu XX (2020) An augmented linear mixing model to address spectral variability for hyperspectral unmixing. IEEE Trans Image Process 29:1–16

    MathSciNet  Google Scholar 

  • Iqbal MJ, Farhan M, Ullah F, Srivastava G, Jabbar S (2023) Intelligent multimedia content delivery in 5G/6G networks: a reinforcement learning approach. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.4842

    Article  Google Scholar 

  • Islam N, Rashid MM, Wibowo S, Xu CY, Morshed A, Wasimi SA, Moore S, Rahman SM (2021) Early weed detection using image processing and machine learning techniques in an Australian chilli farm. Agriculture 11(5):387

    Article  Google Scholar 

  • Khan M, Zhang X, Zhang Q (2020) Use of machine learning tools to extract vegetation health information from UAV hyperspectral data: a review. Environ Ecol Res 8(1):41–49

    Google Scholar 

  • Koger B, Deshpande A, Kerby JT, Graving JM, Costelloe BR, Couzin ID (2023) Quantifying the movement, behaviour, and environmental context of group-living animals using drones and computer vision. J Anim Ecol. 9(3):624

    Google Scholar 

  • Kumar A, Shaikh AM, Li Y et al (2021) Pruning filters with L1-norm and capped L1-norm for CNN compression. Appl Intell 51:1152–1160. https://doi.org/10.1007/s10489-020-01894-y

    Article  Google Scholar 

  • Kwak GH, Park NW (2019) Impact of texture information on crop classification with machine learning and UAV images. Appl Sci 9(4):643

    Article  Google Scholar 

  • Li W, Fu H, Yu L, Cracknell A (2017) Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sens 9(1):22

    Article  Google Scholar 

  • Li L, He H, Huang D, Sun T, Li Q (2019) Improved neural network for spatial–spectral hyperspectral image classification with limited training samples. Int J Remote Sens 40(19):7381–7398

    Google Scholar 

  • Li W, Chen D, Liu Z, Alvarez JM, Fu K, Duan L, Yan G (2020) Deep learning for hyperspectral image classification: An overview. IEEE Trans Geosci Remote Sens 59(6):4790–4807

    Google Scholar 

  • Liu H, Zhang J, Pan Y, Shuai G, Zhu X, Zhu S (2018) An efficient approach based on UAV orthographic imagery to map paddy with support of field-level canopy height from point cloud data. IEEE J Sel Topics Appl Earth Obs Remote Sens 11(6):2034–2046

    Article  Google Scholar 

  • Ma L, Liu Y, Zhang X, Ye Y, Yin G, Johnson BA (2019) Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J Photogramm Remote Sens 152:166–177

    Article  Google Scholar 

  • Muhammad IQ, Majid A, Shamrooz S (2023) Adaptive event-triggered robust H∞ control for Takagi-Sugeno fuzzy networked Markov jump systems with time-varying delay.". Asian Journal of Control. 25(1):213–228

    Article  MathSciNet  Google Scholar 

  • Nijhawan R, Das I, Srivastava PK (2019) Deep learning based land use land cover classification using drones imagery. Eur J Remote Sens 52:294–307

    Google Scholar 

  • Petrides P, Kolios P, Kyrkou C (2020) Detection and classification of agricultural crops from UAV imagery using a deep learning workflow. Remote Sens 12(16):2530

    Google Scholar 

  • Selvaraj MG, Vergara A, Montenegro F, Ruiz HA, Safari N, Raymaekers D, Ocimati W, Ntamwira J, Tits L, Omondi AB, Blomme G (2020) Detection of banana plants and their major diseases through aerial images and machine learning methods: a case study in DR Congo and Republic of Benin. ISPRS J Photogramm Remote Sens 169:110–124

    Article  Google Scholar 

  • Shamrooz M, Li Q, Hou J (2021) Fault detection for asynchronous T-S fuzzy networked Markov jump systems with new event-triggered scheme. IET Control Theory Appl 15(11):1461–1473

    Article  MathSciNet  Google Scholar 

  • Ullah R, Dai X, Sheng A (2020) Event-triggered scheme for fault detection and isolation of non-linear system with time-varying delay. IET Control Theory Appl 14(16):2429–2438

    Article  MathSciNet  Google Scholar 

  • Wang J, Gong W, Wen H, Wang Q, Zhang Y, Fu R (2019a) Object detection in remote sensing images based on a multi-scale convolutional neural network. Sensors 19(11):2482

    Google Scholar 

  • Wang L, Zhai Q, Yin B, et al. (2019) "Second-order convolutional network for crowd counting." Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980T. Doi: https://doi.org/10.1117/12.2540362.

  • Wu J, Potamias M, Zervakis M, Kuo S (2019) Waterbody detection from UAS imagery applying lightweight deep learning models. Remote Sens 11(13):1585

    Google Scholar 

  • Xu H, Sun Z, Cao Y et al (2023) A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things. Soft Comput. https://doi.org/10.1007/s00500-023-09037-4

    Article  Google Scholar 

  • Yao W, Guo Y, Wu Y, Guo J (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In 2017 36th Chinese Control Conference (CCC). IEEE. p. 4192–4197. Doi: https://doi.org/10.23919/ChiCC.2017.8028015.

  • Yin B, Khan J, Wang L, Zhang J, Kumar A (2019) Real-time lane detection and tracking for advanced driver assistance systems. Chin Control Conf IEEE. https://doi.org/10.23919/ChiCC.2019.8866334

    Article  Google Scholar 

  • Yuan C, Zhang Y, Liu Z (2019) A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can J for Res 50(4):383–395

    Google Scholar 

  • Yuan Q, Shen H, Li T, Li Z, Li S, Jiang Y (2020) Deep learning in environmental remote sensing: achievements and challenges. Remote Sens Environ 241:111716

    Article  Google Scholar 

  • Zhu XX, Tuia D, Mou L, Xia GS, Zhang L, Xu F, Fraundorfer F (2017) Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci Remote Sens Mag 5(4):8–36

    Article  Google Scholar 

  • Zhu P et al (2021) ‘Detection and tracking meet drones challenge.’ IEEE Trans Pattern Anal Mach Intell 44(11):7380–7399

    Article  Google Scholar 

Download references

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

  1. School of Architectural Engineering, Shaoxing University Yuanpei College, Shaoxing, 312000, Zhejiang, China

    Peng Li

  2. School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210042, China

    Jawad Khan

Authors
  1. Peng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jawad Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Li.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, P., Khan, J. Feature extraction and analysis of landscape imaging using drones and machine vision. Soft Comput 27, 18529–18547 (2023). https://doi.org/10.1007/s00500-023-09352-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09352-w

Keywords

Search

Navigation