1.
Agüera-Vega JR, Carvajal-Ramírez F, Marques da Silva F, Martínez-Carricondo P, Serrano J, Moral FJ. Evaluation of fire severity indices based on pre- and post-fire multispectral imagery sensed from UAV.
Remote Sens. 2019,
11, 993. [
Google Scholar]
2.
Yang T, Li D, Bai Y, Zhang F, Li S, Wang M, et al. Multiple-object-tracking algorithm based on dense trajectory voting in aerial videos.
Remote Sens. 2019,
11, 2278. [
Google Scholar]
3.
Yao H, Qin R, Chen X. Unmanned aerial vehicle for remote sensing applications - A review.
Remote Sens. 2019,
11, 1443. [
Google Scholar]
4.
Campbell JB, Wynne RH, Thomas VA. Introduction to Remote Sensing, 6th ed.; Guilford Press: New York, NY, USA, 2011.
5.
Tariq R, Rahim M, Aslam N, Bawany N, Faseeha U. DronAID: A smart human detection drone for rescue. In Proceedings of the 2018 15th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT), Islamabad, Pakistan, 8–10 October 2018; pp. 33–37.
6.
Nabeel MM, Al-Shammari S. Fire detection using unmanned aerial vehicle.
Al-Iraqia J. Sci. Eng. Res. 2023,
2, 47–56. [
Google Scholar]
7.
Boesch R. Thermal remote sensing with UAV-based workflows. In Proceedings of the International Conference on Unmanned Aerial Vehicles in Geomatics, Bonn, Germany, 4–7 September 2017; pp. 41–46.
8.
Baena S, Moat J, Whaley O, Boyd DS. Identifying species from the air: UAVs and the very high-resolution challenge for plant conservation.
PLoS ONE 2017,
12, 0188714. [
Google Scholar]
9.
Schedl DC, Kurmi I, Bimber O. Search and rescue with airborne optical sectioning.
Nat.Mach. Intell. 2020,
2, 783–790. [
Google Scholar]
10.
Jónsson SB. RGB and multispectral UAV image classification of agricultural fields using a machine learning algorithm. Master’s Thesis, Lund University, Lund, Sweden, June 2018.
11.
Taha B, Shoufan A. Machine learning-based drone detection and classification: State-of-the-art in research.
IEEE Access 2019,
7, 138669–138682. [
Google Scholar]
12.
Shin J, Seo W, Kim T, Park J, Woo C. Using UAV multispectral images for classification of forest burn severity: A case study of the 2019 Gangneung forest fire.
Forests 2019,
10, 1025. [
Google Scholar]
13.
Auccahuasi W, Bernardo M, Núñez EO, Sernaque F, Castro P, Raymundo L. Analysis of chromatic characteristics, in satellite images for the classification of vegetation covers and deforested areas. In Proceedings of the 2018 2nd International Conference on Video and Image Processing, Hong Kong, China, 29 December 2018; pp. 134–139.
14.
Moafa A. Drones detection using smart sensors. Master’s Thesis, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA, April 2020.
15.
Huang Y, Thomson SL, Stephan YM. Multispectral imaging systems for airborne remote sensing to support agricultural production management.
Int. J. Agric. Biol. Eng. 2010,
3, 50–62. [
Google Scholar]
16.
Dash JP, Watt MS, Pearse GD, Heaphy M, Dungey HS. Assessing very high-resolution UAV imagery for monitoring forest health during a simulated disease outbreak.
ISPRS J. Photogramm. Remote Sens. 2017,
131, 1–14. [
Google Scholar]
17.
Tauro F, Petroselli A, Arcangeletti E. Assessment of drone‐based surface flow observations.
Hydrol. Processes 2015,
30, 1114–1130. [
Google Scholar]
18.
Puri V, Nayyar A, Linesh R. Agriculture Drones: A modern breakthrough in precision agriculture.
J. Stat. Manag. Syst. 2017,
20, 507–518. [
Google Scholar]
19.
Giebel G, Paulsen SU, Bange J, la Cour-Harbo ARJ, Mayer S, van der Kroonenberg A, et al. Autonomous Aerial Sensors for Wind Power Meteorology-a pre-project; Aalborg University: Aalborg Øst, Denmark, 2012.
20.
Hentschke M, Pignaton de Freitas E, Hennig CH, Girardi da Veiga IC. Evaluation of altitude sensors for a crop spraying drone.
Drones 2018,
2, 25. [
Google Scholar]
21.
Kays R, Sheppard J, Mclean K, Welch C, Paunescu C, Wang V, et al. Hot Monkey, Cold reality: Surveying rainforest canopy mammals using drone-mounted thermal infrared sensors.
Int. J. Remote Sens. 2019,
40, 407–419. [
Google Scholar]
22.
Chen Z, Wang X, Liang R. RGB-NIR multispectral camera.
Optics Express 2014,
22, 4985–4994. [
Google Scholar]
23.
Brauers J, Aach T. A Color Filter Array Based Multispectral Camera; Workshop Farbbildverarbeitung: Ilmenau, Denmark, 2006.
24.
Tahar KN, Ahmad A, Akib WAAWM, Mohd WMNW. Aerial mapping using autonomous fixed-wing unmanned aerial vehicle. In Proceedings of the 2012 IEEE 8th International Colloquium on Signal Processing and its Applications, Malacca, Malaysia, 23–25 March 2012, pp. 164–168.
25.
Riley S. Capturing and analyzing multispectral UAV imagery to delineate submerged aquatic vegetation on a small urban stream. Master’s Thesis, Syracuse University, 6 June 2019.
26.
Assmann JJ, Kerby JT, Cunliffe AM, Myers-Smith IH. Vegetation monitoring using multispectral sensors - Best practices and lessons learned from high latitudes.
J. Unmanned Vehicle Syst. 2018,
7, 54–75. [
Google Scholar]
30.
Harvey MC, Rowland JV, Luketina KM. Drone with thermal infrared camera provides high-resolution georeferenced imagery of the Waikite geothermal area, New Zealand.
J. Volcanol. Geotherm. Res. 2016,
325, 61–69. [
Google Scholar]
31.
He X, Liu Y, Kumar A, Arman B, Paul E, Fatima S, et al. A Single Sensor-Based Multispectral imaging camera using a narrow spectral band color mosaic integrated on the monochrome CMOS image sensor.
APL Photonics 2020,
5, 046104. [
Google Scholar]
32.
Farlik J, Kratky M, Casar J and Stary V. Multispectral detection of commercial unmanned aerial vehicles.
Sensors 2019,
19, 1517. [
Google Scholar]
33.
Xiang TZ, Xia GS, Zhang L. Mini-unmanned aerial vehicle-based remote sensing: Techniques, applications, and prospects.
IEEE Geosci. Remote Sens. Mag. 2019,
7, 29–63. [
Google Scholar]
34.
Maddikunta PKR, Hakak S, Alazab M, Bhattacharya S, Gadekallu TR, Khan WZ, et al. Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges.
IEEE Sensors J. 2021,
21, 17608–17619. [
Google Scholar]
35.
Fernando HCTE, De Silva ATA, De Zoysa MDC, Dilshan KADC, Munasinghe SR. Modelling, simulation, and implementation of a quadrotor UAV. In Proceedings of the 2013 IEEE 8th International Conference on Industrial and Information Systems, Peradeniya, Sri Lanka, 17–20 December 2013; pp. 207–212.
36.
Schmidt MD. Simulation and control of a quadrotor unmanned aerial vehicle. Master’s Thesis, University of Kentucky, Lexington, KY, USA, April 2018.
37.
Brauers J, Aach T. Geometric calibration of lens and filter distortions for multispectral filter-wheel Cameras.
IEEE Trans. Image Process. 2010,
20, 496–505. [
Google Scholar]
38.
Myung IJ. Tutorial on maximum likelihood estimation.
J. Math. Psychol. 2003,
47, 90–100. [
Google Scholar]
39.
Zhang X, Zhang X, Wang W. Convolutional neural network. In Intelligent Information Processing with Matlab; Springer: Singapore, 2023.
40.
Hassan H, Rahman SA. Integration of aerial photography, Airborne LiDAR, and Airborne IFSAR for Mapping in Malaysia IOP.
Conf. Ser. Earth Environ. Sci. 2021,
767, 012020. [
Google Scholar]
41.
Prakel. Basic Photography 01: Composition, 2nd ed.; AVA Publishing: Lausane, Switzerland, 2012.
42.
Aidil M, Panjaitan SN, Yacoub RR. Design and development of flight controller for quadcopter drone control.
Telecommun. Comput. Electr. Eng. J. 2024,
1, 279–291. [
Google Scholar]
43.
Arora S, Ntantis EL. Customization and payload integration of hexacopter for enhanced grocery delivery.
Multidiscip. Sci. J. 2024,
6, 2024126. [
Google Scholar]