209
Views
2
CrossRef citations to date
0
Altmetric
Drones Paper

Analysis of the application of an advanced classifier algorithm to ultra-high resolution unmanned aerial aircraft imagery – a neural network approach

ORCID Icon, , &
Pages 3266-3286 | Received 31 Oct 2018, Accepted 24 Jul 2019, Published online: 06 Nov 2019

References

  • Atkinson, P. M., and A. R. L. Tatnall. 1997. “Introduction Neural Networks in Remote Sensing.” International Journal of Remote Sensing 18 (4): 699–709. doi:10.1080/014311697218700.
  • Chan, J. C. W., J. C. W. Chan, K. P. Chan, K. P. Chan, and A. G. O. Yeh. 2001. “Detecting the Nature of Change in an Urban Environment: A Comparison of Machine Learning Algorithms.” Photogrammetric Engineering and Remote Sensing 67 (2): 213–225.
  • Civco, D. L. 1993. “Artificial Neural Networks for Land-Cover Classification and Mapping.” International Journal of Geographical Information Systems 7 (2): 173–186. doi:10.1080/02693799308901949.
  • Clothier, R. A., and R. A. Walker. 2015. “Safety Risk Management of Unmanned Aircraft Systems.” In Handbook of Unmanned Aerial Vehicles, edited by K. Valavanis and G. Vachtsevanos, 2229–2275. Dordrecht: Springer.
  • Corsini, G., M. Diani, R. Grasso, M. De Martino, P. Mantero, and S. B. Serpico. 2003. “Radial Basis Function and Multilayer Perceptron Neural Networks for Sea Water Optically Active Parameter Estimation in case II Waters: A Comparison.” International Journal of Remote Sensing 24 (20): 3917–3932. doi:10.1080/0143116031000103781.
  • Foody, G. M. 2004. “Supervised Image Classification by MLP and RBF Neural Networks with and without an Exhaustively Defined Set of Classes.” International Journal of Remote Sensing 25 (15): 3091–3104. doi:10.1080/01431160310001648019.
  • Gašparović, M., and D. Gajski. 2016. “Two-Step Camera Calibration Method Developed for Micro UAV`s.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI (July): 829–833. doi:10.5194/isprsarchives-XLI-B1-829-2016.
  • Gašparović, M., and T. Jogun. 2018. “The Effect of Fusing Sentinel-2 Bands on Land-Cover Classification.” International Journal of Remote Sensing 39 (3): 822–841. doi:10.1080/01431161.2017.1392640.
  • Gašparović, M., and L. Jurjevi. 2017. “Gimbal Influence on the Stability of Exterior Orientation Parameters of UAV Acquired Images.” Sensors 7–9. doi:10.3390/s17020401.
  • Gašparović, M., A. Seletković, A. Berta, and I. Balenović. 2017. “The Evaluation of Photogrammetry-Based DSM from Low- Cost UAV by LiDAR-Based DSM.” South-East European Forestry 8 (2): 117–125. doi:10.15177/seefor.17-16.
  • Haykin, S. 1998. Neural Networks: A Comprehensive Foundation. 2nd ed. Upper Saddle River, NJ: Prentice Hall. ISBN 0-13-273350-1.
  • Hepner, G. F., T. Logan, N. Pitter, and N. Bryant. 1989. “Artificial Neural Network Classification Using a Minimal Training Set: Comparison to Conventional Supervised Classification” Photogrammetric Engineering and Remote Sensing 56 (4): 469–473. https://www.asprs.org/wp-content/uploads/pers/1990journal/apr/1990_apr_469-473.pdf
  • Hornik, K., M. Stinchcombe, and H. White. 1989. “Multilayer Feedforward Networks are Universal Approximators.” Neural Networks 2: 359–366. doi:10.1016/0893-6080(89)90020-8.
  • Hu, F., G. Xia, J. Hu, and L. Zhang. 2015. “Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery.” Remote Sensing 14680–14707. doi:10.3390/rs71114680.
  • Indonesian Database of Higher Education. 2018. “University Profile.” Accessed 27 July 2018. https://forlap.ristekdikti.go.id/perguruantinggi/detail/OTlBM0I3QUYtNjQ3MC00RDE4LThCMDYtMDk5NDFFNjYzQjA3
  • Kavzoglu, T., and P. M. Mather. 2003. “The Use of Backpropagating Artificial Neural Networks in Land Cover Classification.” International Journal of Remote Sensing 24 (23): 4907–4938. doi:10.1080/0143116031000114851.
  • Li, L., J. Ma, and Q. Wen. 2008. “Comparison of Local Transfer Function Classifier and Radial Basis Function Neural Network with and without an Exhaustively Defined Set of Classes.” International Journal of Remote Sensing 30 (1): 85–96. doi:10.1080/01431160802261189.
  • Liang, H., and Q. Li. 2016. “Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural.” Remote Sensing. doi:10.3390/rs8020099.
  • Lu, B., and Y. He. 2017. “ISPRS Journal of Photogrammetry and Remote Sensing Species Classification Using Unmanned Aerial Vehicle (UAV) -acquired High Spatial Resolution Imagery in a Heterogeneous Grassland.” ISPRS Journal of Photogrammetry and Remote Sensing 128: 73–85. doi:10.1016/j.isprsjprs.2017.03.011.
  • Melgani, F., and L. Bruzzone. 2004. “Classification of Hyperspectral Remote Sensing.” IEEE Transaction on Geoscience and Remote Sensing 42: 1778–1790. doi:10.1109/TGRS.2004.831865.
  • Miller, D. M., E. J. Kaminsky, and S. Rana. 1995. “Neural Network Classification of Remote-Sensing Data.” Computers and Geosciences 21 (3): 377–386. doi:10.1016/0098-3004(94)00082-6.
  • Paola, J. D., and R. A. Schowengerdt. 1995. “A Detailed Comparison of Backpropagation Neural Network and Maximum-likelihood Classifiers for Urban Land Use Classification.” IEEE Transactions on Geoscience and Remote Sensing 33 (4): 981–996. doi:10.1109/36.406684.
  • Pontius, R. G., Jr. 2000. “Quantification Error versus Location Error in Comparison of Categorical Maps.” Photogrammetric Engineering and Remote Sensing 66 (8): 1011–1016.
  • Pontius, R. G., Jr, and M. L. Cheuk. 2006. “A Generalized Cross-tabulation Matrix to Compare Soft-classified Maps at Multiple Resolutions.” International Journal of Geographical Information Science 20 (1): 1–30. doi:10.1080/13658810500391024.
  • Powell, M. J. D. 1987. “Radial Basis Functions for Multivariate Interpolation: A Review.” In Algorithms for Approximation, edited by J. Mason and M. Cox, 143–167. Oxford: Clarendon Press.
  • Ramdani, F. 2013a. “Urban Vegetation Mapping from Fused Hyperspectral Image and LiDAR Data with Application to Monitor Urban Tree Heights.” Journal of Geographic Information System 5: 404–408. doi:10.4236/jgis.2013.54038.
  • Ramdani, F. 2013b. “Extraction of Urban Vegetation in Highly Dense Urban Environment with Application to Measure Inhabitants ’ Satisfaction of Urban Green Space.” Journal of Geographic Information System 5: 117–122. doi:10.4236/jgis.2013.52012.
  • Ramdani, F., and P. Setiani. 2013. “Spatio-Temporal Analysis of Urban Temperature in Bandung City, Indonesia.” Urban Ecosystems, November. doi:10.1007/s11252-013-0332-1.
  • Rebetez, J., H. F. Satiz´abal, M. Mota, D. Noll, L. Buchi, M. Wendling, B. Cannelle, A. Perez-Uribe, and S. Burgos. 2016. “Augmenting A Convolutional Neural Network with Local Histograms - A Case Study in Crop Classification from High-Resolution UAV Imagery.” European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning April: 27–29.
  • Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. “Learning Internal Representations by Error Propagation.” In Parallel Distributed Processing, edited by D. Rumelhart, J. McClelland, and P. R. Group, 318–362. Vol. 1. Cambridge, MA: MIT Press.
  • Sarimveis, H., P. Doganis, and A. Alexandridis. 2006. “A Classification Technique Based on Radial Basis Function Neural Networks.” Advances in Engineering Software 37 (4): 218–221. doi:10.1016/j.advengsoft.2005.07.005.
  • Scarpa, G., M. Gargiulo, A. Mazza, and R. Gaetano. 2018. “A CNN-Based Fusion Method for Feature Extraction from Sentinel Data.” Remote Sensing 1–20. doi:10.3390/rs10020236.
  • Serpen, G., H. Jiang, and L. Allred. 1997. “Performance Analysis of Probabilistic Potential Function Neural Network Classifier.” In Proceedings of Artificial Neural Networks in Engineering Conference, 471–476. St. Louis, MO: Citeseer.
  • Song, X., Z. Duan, and X. Jiang. 2011. “Comparison of Artificial Neural Networks and Support Vector Machine Classifiers for Land Cover Classification in Northern China Using a SPOT-5 HRG Image.” International Journal of Remote Sensing 33 (10): 3301–3320. doi:10.1080/01431161.2011.568531.
  • Watts, A. C., V. G. Ambrosia, E. A. Hinkley, and M. Field. 2012. “Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use.” Remote Sensing 1671–1692. doi:10.3390/rs4061671.
  • Zhang, Y., J. Gao, and J. Wang. 2007. “Detailed Mapping of A Salt Farm from Landsat TM Imagery Using Neural Network and Maximum Likelihood Classifiers: A Comparison.” International Journal of Remote Sensing 28 (10): 2077–2089. doi:10.1080/01431160500406870.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.