DOI: https://doi.org/10.14710/geoplanning.11.1.17-30

Assessment of Random Forest and Neural Network for Improving Land Use/ Land Cover Mapping from LIDAR Data and RGB Image: A Case Study of Magaga-El-Menia Governorate, Egypt

*Lamyaa Gamal EL-Deen Taha  -  National authority of remote sensing and space sciences, Cairo, Egypt, Egypt
Asmaa Ahmed Mandouh  -  National authority of remote sensing and space sciences, Cairo, Egypt, Egypt

Citation Format:
Abstract

The goals of this article are to improve classification of land use/land cover information using LIDAR data and RGB images, as well as to compare the performance of various supervised machine learning classifiers (random forest and neural network) for extracting land use/land cover information. The 3D coordinates are first transferred to a high-resolution raster via interpolation. Height and intensity raster grids are formed. Second, various raster maps - a normalized digital surface model (nDSM), the difference of returns, and the LiDAR intensity image -are combined to create a multi-channel image. Five scenarios with different combinations were created. Finally, on the five separate datasets, several classifications based on random forest and neural network classifiers were performed. The classification findings were subjected to a quantitative accuracy check. A comparison of these five methodologies has been conducted. Following that, morphological operations were used to eliminate noise. The results revealed also that the fourth approach is the best followed by the third approach then the last approach then the second approach followed by the first approach. It was discovered that random forest classification outperforms neural network classification in terms of classification accuracy.

Fulltext View|Download
Keywords: Feature Detection, Intensity, Neural Network, Machine Learning
Funding: National authority of remote sensing and space sciences

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
Assessment of Random Forest and Neural Network for Improving Land Use/ Land Cover Mapping from LIDAR Data and RGB Image: A Case Study of Magaga-El-Menia Governorate, Egypt Comparison of Land Cover Change Prediction Models: A Case Study in Kedungkandang District, Malang City Mapping Landslide Vulnerability using Machine Learning Approach along the Taba Penanjung-Kepahiang Road, Bengkulu Province More recent articles
Most cited articles
THE DEVELOPMENT OF MARINE SPATIAL PLANNING AND ITS APPLICATION FOR FLOATING FISH NET CULTURE Geometric Accuracy Assessment for Shoreline Derived from NDWI, MNDWI, and AWEI Transformation on Various Coastal Physical Typology in Jepara Regency using Landsat 8 OLI Imagery in 2018 ASSESSMENT OF MANGROVE FOREST DEGRADATION THROUGH CANOPY FRACTIONAL COVER IN KARIMUNJAWA ISLAND, CENTRAL JAVA, INDONESIA A SPATIAL ANALYSIS FOR ASSESSING THE SUITABILITY OF ELEMENTARY SCHOOL AS SOCIAL INFRASTRUCTURE AT THE NEIGHBOURHOOD UNIT SCALE IN SUPPORTING CHILD-FRIENDLY SURAKARTA SLUM REVITALIZING PLAN OF BAGHDADIYAH BY SPATIAL RE-MODELING CONFIGURATION More cited articles

  1. https://pubmed.ncbi.nlm.nih.gov/?term=Alshari%20EA%5BAuthor%5D">Alshari E. A., https://pubmed.ncbi.nlm.nih.gov/?term=Abdulkareem%20MB%5BAuthor%5D">Abdulkareem M. B. ,https://pubmed.ncbi.nlm.nih.gov/?term=Gawali%20BW%5BAuthor%5D">Gawali B.W., (2023) Classification of Land Use/Land Cover using Artificial Intelligence (ANN-RF). Frontiers in Artificial Intelligence, 5, 964279. https://doi.org/10.3389/frai.2022.964279">[Crossref]

  2. Akar, Ö., & Güngör, O. (2012). Classification of Multispectral Images using Random Forest algorithm. Journal of Geodesy and Geoinformation, 1(2), 105-112. https://doi.org/%2010.9733/jgg.241212.1">[Crossref]

  3. Ambinakudige, S., & Intsiful, A. (2022). Estimation of area and volume change in the glaciers of the Columbia Icefield, Canada using machine learning algorithms and Landsat images. Remote Sensing Applications: Society and Environment, 26, 100732.https://doi.org/10.1016/j.rsase.2022.100732">[Crossref]

  4. Antonarakis, A. S., Richards, K. S., & Brasington, J. (2008). Object-based land cover classification using airborne LiDAR. Remote Sensing of environment, 112(6), 2988-2998.. https://doi.org/10.1016/j.rse.2008.02.004">[Crossref]

  5. Axelsson, P. (1999). Processing of laser scanner data—algorithms and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3), 138-147.https://doi.org/10.1016/S0924-2716(99)00008-8">[Crossref]

  6. Azizi, Z., Najafi, A., & Sadeghian, S. (2014). Forest road detection using LiDAR data. Journal of forestry research, 25, 975-980. https://doi.org/10.1007/.%20-014-054.4-0.">[Crossref]

  7. Bartels, M., & Wei, H. (2006, September). Rule-based improvement of maximum likelihood classified LIDAR data fused with co-registered bands. In Annual Conference of the Remote Sensing and Photogrammetry Society, CD Proceedings (Vol. 5, pp. 1-9).

  8. Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31. https://doi.org/10.1016/j.isprsjprs.2016.01.011">[Crossref]

  9. Brennan, R., & Webster, T. L. (2006). Object-Oriented Land Cover Classification of Lidar-Derived Surfaces. Canadian Journal of Remote Sensing, 32(2), 162-172. https://doi.org/10.5589/m06-015">[Crossref]

  10. Chehata, N., Guo, L., & Mallet, C. (2009, September). Airborne LIDAR Feature Selection for Urban Classification using Random Forests. In Laserscanning.

  11. Chen, W., Li, X., Wang, Y., Chen, G., & Liu, S. (2014). Forested Landslide Detection using LiDAR Data and the Random Forest Algorithm: A Case Study of the Three Gorges, China. Remote Sensing of Environment, 152, 291-301. https://doi.org/10.1016/j.rse.2014.07.004">[Crossref]

  12. Christovam, L. E., Pessoa, G. G., Shimabukuro, M. H., & Galo, M. L. B. T. (2019). Land Use and Land Cover Classification Using Hyperspectral Imagery: Evaluating the Performance of Spectral Angle Mapper, Support Vector Machine and Random Forest. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 1841-1847.https://doi.org/10.5194/isprs-archives-XLII-2-W13-1841-2019">[Crossref]

  13.  Cobby, D. M., Mason, D. C., Horritt, M. S., & Bates, P. D. (2003). Two‐dimensional hydraulic flood modelling using a finite‐element mesh decomposed according to vegetation and topographic features derived from airborne scanning laser altimetry. Hydrological processes, 17(10), 1979-2000.. https://doi.org/10.1002/hyp.1201">[Crossref]

  14. Corcoran, J. M., Knight, J. F., & Gallant, A. L. (2013). Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota. Remote Sensing, 5(7), 3212-3238. https://doi.org/10.3390/rs5073212">[Crossref]

  15. Dahinden, C.,. (2009). An improved Random Forests Approach with Application to the Performance Prediction Challenge Datasets.  Hands-on Pattern Recognition.

  16. Diab, A., Kashef, R., & Shaker, A. (2022). Deep Learning for LiDAR Point Cloud Classification in Remote Sensing. Sensors, 22(20), 7868.https://doi.org/10.3390/s22207868">[Crossref]

  17. Dixit, A., & Agarwal, S. (2020). Super-Resolution Mapping of Hyperspectral Data using Artificial Neural Network and Wavelet. Remote Sensing Applications: Society and Environment, 20, 100374.https://doi.org/10.1016/j.rsase.2020.100374">[Crossref]

  18. Foody, G. M. (1999). Image classification with a neural network: from completely-crisp to fully-fuzzy situations. Advances in remote sensing and GIS analysis, 17-37.

  19. Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern recognition letters, 27(4), 294-300.. https://doi.org/10.1016/j.patrec.2005.08.011">[Crossref[

  20. Guan, H., Yu, J., Li, J., & Luo, L. (2012). Random forests-based feature selection for land-use classification using lidar data and orthoimagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, 203-208.https://doi.org/10.5194/isprsarchives-XXXIX-B7-203-2012">[Crossref]

  21. Haala, N., & Brenner, C. (1999). Extraction of buildings and trees in urban environments. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3), 130-137. . https://doi.org/10.1016/S0924-2716(99)00010-6">[Crossref]

  22. Hartfield, K. A., Landau, K. I., & Van Leeuwen, W. J. (2011). Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat. Remote Sensing, 3(11), 2364-2383.https://doi.org/10.3390/rs3112364">[Crossref]

  23. Horning, N.,.(2010). Random Forests: An Algorithm for Image Classification and Generation of Continuous Fields Data Sets. In International Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences

  24. Huang, M. J., Shyue, S. W., Lee, L. H., & Kao, C. C. (2008). A Knowledge-Based Approach to Urban Feature Classification using Aerial Imagery with LIDAR Data. Photogrammetric Engineering & Remote Sensing, 74(12), 1473-1485. https://doi.org/10.14358/PERS.74.12.1473">[Crossref]

  25. Hugo, C., Capao, L., Fernando, B. and Mario,  C.,.(2007).Meris Based Land Cover Classification with Self-Organizing Maps: preliminary results’’ EARSeL SIG Remote Sensing of Land Use & Land Cover.

  26. Im, J., Jensen, J. R., & Hodgson, M. E. (2008). Object-Based Land Cover Classification using High-Posting-Density LiDAR data. GIScience & Remote Sensing, 45(2), 209-228.https://doi.org/10.2747/1548-1603.45.2.209">[Crossref]

  27. Jensen, J. R. (2005). Digital image processing: a remote sensing perspective. Upper Saddle River, NJ: sPrentice Hall.

  28. Li, X., Cheng, X., Chen, W., Chen, G., & Liu, S. (2015). Identification of Forested Landslides using LIDAR Data, Object-Based Image Analysis, and Machine Learning Algorithms. Remote Sensing, 7(8), 9705-9726.https://doi.org/10.3390/rs70809705">[Crossref]

  29. Lu, Y. H., & Trinder, J. (2003, May). Data Fusion Applied to Automatic Building Extraction in 3D reconstruction. In Proc. ASPRS Conference (pp. 114-122).

  30. Mason, D. C., Cobby, D. M., Horritt, M. S., & Bates, P. D. (2003). Floodplain Friction Parameterization in Two‐Dimensional River Flood Models using Vegetation Heights Derived from Airborne Scanning Laser Altimetry. Hydrological Processes, 17(9), 1711-1732.. https://doi.org/10.7848/ksgpc.2011.29.4.429">[Crossref]

  31. Minh, N. Q., Hien, L. P,.(2011).’Land cover classification using LiDAR intensity data and neural network’. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography.29(4),429–438. https://doi.org/10.7848/ksgpc.2011.29.4.429">[Crossref]

  32. Mishra, V. N., Prasad, R., Kumar, P., Gupta, D. K., & Srivastava, P. K. (2017). Dual-Polarimetric C-band SAR Data for Land Use/Land Cover Classification by incorporating textural information. Environmental Earth Sciences, 76, 1-16. https://doi.org/10.1007/s12665-016-6341-7">[Crossref]

  33. Nguyen, M. Q., Atkinson, P. M., & Lewis, H. G. (2005). Superresolution Mapping using a Hopfield Neural Network with LIDAR data. IEEE Geoscience and Remote Sensing Letters, 2(3), 366-370.. https://doi.org/10.1109/LGRS.2005.851551">[Crossref]

  34. Niemeyer, J., Rottensteiner, F., & Soergel, U. (2014). Contextual Classification of LIDAR Data and Building Object Detection in Urban Areas. Isprs Journal of Photogrammetry and Remote Sensing, 87, 152-165.. https://doi.org/10.1016/j.isprsjprs.2013.11.001">[Crossref]

  35. Packianather, M. S., & Drake, P. R. (2005). Comparison of Neural and Minimum Distance Classifiers in Wood Veneer Defect Identification. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 219(11), 831-841.. https://doi.org/10.1243/095440505X32823">[Crossref]

  36. Pan, S., Guan, H., Chen, Y., Yu, Y., Gonçalves, W. N., Junior, J. M., & Li, J. (2020). Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 241-254.. https://doi.org/10.1016/j.isprsjprs.2020.05.022">[Crossref]

  37. Priestnall, G., Jaafar, J., & Duncan, A. (2000). Extracting Urban Features from LiDAR Digital Surface Models. Computers, Environment and Urban Systems, 24(2), 65-78.https://doi.org/10.1016/S0198-9715(99)00047-2">[Crossref]

  38. Rashdi, R., Balado Frías, J., Martínez Sánchez, J., & Arias Sánchez, P. (2023, May). Comparative study of road and urban object classification based on mobile laser scanners. In 12th International Symposium on Mobile Mapping Technology (MMT 2023), Padua, Italia, 24-26 mayo 2023. Enxeñaría dos recursos naturais e medio ambiente. https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-423-2023">[Crossref]

  39. Rodriguez-Galiano, V. F., & Chica-Rivas, M. (2014). Evaluation of Different Machine Learning Methods for land cover mapping of a Mediterranean Area Using Multi-Seasonal Landsat Images and Digital Terrain Models. International Journal of Digital Earth, 7(6), 492-509.https://doi.org/10.1080/17538947.2012.748848">[Crossref]

  40. Rottensteiner, F., Trinder, J., Clode, S., & Kubik, K. (2005). Using the Dempster–Shafer Method for the Fusion of LIDAR Data and Multi-Spectral Images for Building Detection. Information Fusion, 6(4), 283-300.https://doi.org/10.1016/j.inffus.2004.06.004">[Crossref]

  41. Schenk, T., & Csathó, B. (2002). Fusion of LIDAR Data and Aerial Imagery for a More Complete Surface Description. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34(3/A), 310-317.

  42. https://ui.adsabs.harvard.edu/search/q=author:%22Sharma%2C+M.%22&sort=date%20desc,%20bibcode%20desc">Sharma M. ,https://ui.adsabs.harvard.edu/search/q=author:%22Garg%2C+R.+D.%22&sort=date%20desc,%20bibcode%20desc">Garg, R. D.   https://ui.adsabs.harvard.edu/search/q=author:%22Badenko%2C+V.%22&sort=date%20desc,%20bibcode%20desc">Badenko V. , https://ui.adsabs.harvard.edu/search/q=author:%22Fedotov%2C+A.%22&sort=date%20desc,%20bibcode%20desc">Fedotov A. ,   https://ui.adsabs.harvard.edu/search/q=author:%22Ojha%2C+C.+S.+P.%22&sort=date%20desc,%20bibcode%20desc">Ojha C. S. P. ,  https://ui.adsabs.harvard.edu/search/q=author:%22Liu%2C+M.%22&sort=date%20desc,%20bibcode%20desc">Liu M. , https://ui.adsabs.harvard.edu/search/q=author:%22Yao%2C+A.%22&sort=date%20desc,%20bibcode%20desc">Yao,( 2020 ) ‘’A LiDAR  Data Classification Using Machine Learning Methods’’American Geophysical Union, Fall Meeting 2020. https://doi.org/10.14710/geoplanning.11.1.%25p">[Crossref]

  43. Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., & Homayouni, S. (2020). Support Vector Machine Versus Random Forest For Remote Sensing Image Classification: A Meta-Analysis and systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308-6325.https://doi.org/10.1109/JSTARS.2020.3026724">[Crossref]

  44. Song, J. H., Han, S. H., Yu, K. Y., & Kim, Y. I. (2002). Assessing the Possibility of Land-Cover Classification using LIDAR Intensity Data. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34(3/B), 259-262.

  45. Tan, J., Zuo, J., Xie, X., Ding, M., Xu, Z., & Zhou, F. (2021). MLAs Land Cover Mapping Performance Across Varying Geomorphology with Landsat OLI-8 and Minimum Human Intervention. Ecological Informatics, 61, 101227.  https://doi.org/10.1016/j.ecoinf.2021.101227">[Crossref]

  46. Tokar, O., Vovk, O., Kolyasa, L., Havryliuk, S., & Korol, M. (2018, September). Using the Random Forest classification for land cover interpretation of Landsat images in the Prykarpattya region of Ukraine. In 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 1, pp. 241-244). IEEE.https://doi.org/10.1109/STC-CSIT.2018.8526646">[Crossref]

  47. Uzar, M. (2014). Automatic Building Extraction with Multi-Sensor Data using Rule-Based Classification. European Journal of Remote Sensing, 47(1), 1-18.https://doi.org/10.5721/EuJRS20144701">[Crossref]

  48. Wang, C., Shu, Q., Wang, X., Guo, B., Liu, P., & Li, Q. (2019). A Random Forest Classifier based on Pixel Comparison Features for Urban LiDAR Data. ISPRS journal of photogrammetry and remote sensing, 148, 75-86. https://doi.org/10.1016/j.isprsjprs.2018.12.009">[Crossref]

  49. Wijaya, M. S., Kamal, M., Widayani, P., & Arjasakusuma, S. (2023). Classification of Mangrove Vegetation Structure using Airborne LiDAR in Ratai Bay, Lampung Province, Indonesia. Geoplanning: Journal of Geomatics and Planning, 10(2), 123-134.. https://doi.org/10.14710/geoplanning.10.2.123-134">[Crossref]

  50. Wong, S. N., & Sarker, M. L. R. (2014). Land Use/Land Cover Mapping using Multi-Scale Texture Processing of High Resolution Data. In IOP Conference Series: Earth and Environmental Science (Vol. 18, No. 1, p. 012185). IOP Publishing. https://doi.org/10.1088/1755-1315/18/1/012185">[Crossref]

  51. Xie, G. (2023). Machine Learning methods and Land Use/Land Cover (LULC) in the coastal Pays de Brest (Doctoral dissertation, Université de Bretagne occidentale-Brest).

  52. Yan, W. Y., Shaker, A., & El-Ashmawy, N. (2015). Urban Land Cover Classification using Airborne LiDAR data: A review. Remote Sensing of Environment, 158, 295-310.https://doi.org/10.1016/j.rse.2014.11.001%20%20%202017">[Crossref]

  53. Yusof, N., Shafri, H. Z. M., & Shaharum, N. S. N. (2021). The Use of Landsat-8 and Sentinel-2 Imageries in Detecting and Mapping Rubber Trees. Journal of Rubber Research, 24, 121-135.. https://doi.org/10.1007/s42464-020-00078-0">[Crossref]

  54. Zeng,  Q . , (2008). Data filtering and feature extraction of urban typical objects from airborne LIDAR point cloud ‘’The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences. 37.Part B3b. Beijing

  55. Zhou, W., Huang, G., Troy, A., & Cadenasso, M. L. (2009). Object-Based Land Cover Classification of Shaded Areas In High Spatial Resolution Imagery of Urban Areas: A Comparison Study. Remote Sensing of Environment, 113(8), 1769-1777https://doi.org/10.1016/j.rse.2009.04.007">.[Crossref]

  56. Zhu, L., Shortridge, A. M., & Lusch, D. (2012). Conflating LiDAR Data and Multispectral Imagery for Efficient Building Detection. Journal of Applied Remote Sensing, 6(1), 063602-063602.. https://doi.org/10.1117/1.JRS.6.063602">[Crossref]


Last update:

No citation recorded.

Last update: 2024-12-24 18:14:38

No citation recorded.