Image-Based Classification of Rice Field Conversion: A Comparison Between MLP and SVM Using Multispectral Features
DOI:
https://doi.org/10.52436/1.jutif.2025.6.5.5179Keywords:
Land Use Change, Machine Learning, Multilayer Perceptron (MLP), Multispectral Remote Sensing, Rice Field Conversion, Support Vector Machine (SVM)Abstract
The conversion of farmland into non-agricultural purposes has emerged as a pressing concern in many urban regions, including Koto Tangah District, Padang City. In this area, agricultural land experienced a 4% shift in land use between 2022 and 2024. If this trend continues, it could lead to a notable decline in rice production and ultimately threaten food security. This research focuses on examining spatial transformations of rice fields from 2022 to 2024 by utilizing Sentinel-2 satellite imagery along with advanced classification techniques. Vegetation and moisture features were extracted using NDVI, NDWI, texture analysis through GLCM, and Principal Component Analysis (PCA). To classify land cover changes and assess model accuracy, two machine learning approaches were applied: Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The findings reveal a considerable reduction in dense vegetation, indicated by the downward shift of NDVI values in 2024. MLP achieved an accuracy of 82%, outperforming SVM, which reached 71%. Furthermore, MLP obtained a higher F1-score for non-rice field detection (0.75 vs. 0.74) and produced more realistic delineations of rice field boundaries during spatial validation. These outcomes highlight the potential of MLP in monitoring land use conversion, supporting agricultural land conservation, and guiding sustainable urban planning. Moreover, the study contributes to computer science by advancing the use of machine learning for spatio-temporal analysis and reinforcing the role of non-linear models in satellite image classification.
Downloads
References
V. Mutiara, R. Febriamansyah, R. Hariance, and A. S. Utami, “Farmers’ resilience towards land use change case study in Padang City, West Sumatra, Indonesia),” IOP Conf. Ser.: Earth Environ. Sci., vol. 583, no. 1, p. 012014, Oct. 2020, doi: 10.1088/1755-1315/583/1/012014
BPS-Statistics Indonesia West Sumatera, Paddy and Rice Production by Regency/City in West Sumatra Province. [Online]. Available: https://sumbar.bps.go.id/en/statistics-table/3/ZDNaak0yODBUVTlGYW5sa2REUkVUVVY1YVZkbmR6MDkjMyMxMzAw/paddy-and-rice-production-sup-1--sup--by-regency-municipality-in-sumatera-barat-province.html?year=2022. (Accessed: Sept. 01, 2025)
J. Oakleaf et al., “Mapping global land conversion pressure to support conservation planning,” Sci Data, vol. 11, no. 1, July 2024, doi: 10.1038/s41597-024-03639-9
J. C. Onwe, M. G. Ojide, M. Subhan, and D. Forgenie, “Food security in Nigeria amidst globalization, economic expansion, and population growth: A wavelet coherence and QARDL analysis,” Journal of Agriculture and Food Research, vol. 18, p. 101413, Dec. 2024, doi: 10.1016/j.jafr.2024.101413
“Urbanisation, Paddy Field Conversion and its Impact on Rice Production in Indonesia: A Synthesis of Panel Data 2015–2019,” in Advances in Intelligent Systems Research, Dordrecht: Atlantis Press International BV, 2024, pp. 249–262. doi: 10.2991/978-94-6463-445-7_28
S. Zhang et al., “Monitoring the Spatio-Temporal Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in Xinghua,” IEEE Access, vol. 10, pp. 84518–84534, 2022, doi: 10.1109/ACCESS.2022.3197650
Z. Lu, W. Li, and S. Zhou, “Constructing a resilient ecological network by considering source stability in the largest Chinese urban agglomeration,” Journal of Environmental Management, vol. 328, p. 116989, Feb. 2023, doi: 10.1016/j.jenvman.2022.116989
C. Zhao et al., “Evaluating ecological conservation effectiveness of security patterns under multiple scenarios: A case study of Hubei Province,” Ecological Indicators, vol. 166, p. 112528, Sept. 2024, doi: 10.1016/j.ecolind.2024.112528
W. Hu, K. Xiong, M. Zhang, and L. You, “Land use change impact on the ecosystem service value in karst desertification control zone,” npj Heritage Science, vol. 13, no. 1, June 2025, doi: 10.1038/s40494-025-01860-2
“UU No. 41 Tahun 2009,” Database Peraturan | JDIH BPK. [Online]. Available: http://peraturan.bpk.go.id/Details/38786/uu-no-41-tahun-2009. (Accessed: July 13, 2025)
K. Kirby, S. Ferguson, C. D. Rennie, J. Cousineau, and I. Nistor, “Identification of the best method for detecting surface water in Sentinel-2 multispectral satellite imagery,” Remote Sensing Applications: Society and Environment, vol. 36, p. 101367, Nov. 2024, doi: 10.1016/j.rsase.2024.101367
A. Ansari et al., “Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach,” Heliyon, vol. 9, no. 9, p. e19639, Sept. 2023, doi: 10.1016/j.heliyon.2023.e19639
S. Wang, W. Han, X. Huang, X. Zhang, L. Wang, and J. Li, “Trustworthy remote sensing interpretation: Concepts, technologies, and applications,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 209, pp. 150–172, Mar. 2024, doi: 10.1016/j.isprsjprs.2024.02.003
M. Aghaabbasi and S. Chalermpong, “Machine learning techniques for evaluating the nonlinear link between built-environment characteristics and travel behaviors: A systematic review,” Travel Behaviour and Society, vol. 33, p. 100640, Oct. 2023, doi: 10.1016/j.tbs.2023.100640
Y. Yang, T. Nikolaidis, S. Jafari, and P. Pilidis, “Gas turbine engine transient performance and heat transfer effect modelling: A comprehensive review, research challenges, and exploring the future,” Applied Thermal Engineering, vol. 236, p. 121523, Jan. 2024, doi: 10.1016/j.applthermaleng.2023.121523
A. A. Khan, O. Chaudhari, and R. Chandra, “A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation,” Expert Systems with Applications, vol. 244, p. 122778, June 2024, doi: 10.1016/j.eswa.2023.122778
M. A. K. Raiaan et al., “A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks,” Decision Analytics Journal, vol. 11, p. 100470, June 2024, doi: 10.1016/j.dajour.2024.100470
Z. Quan and L. Pu, “An improved accurate classification method for online education resources based on support vector machine (SVM): Algorithm and experiment,” Educ Inf Technol, vol. 28, no. 7, pp. 8097–8111, July 2023, doi: 10.1007/s10639-022-11514-6
H. Li, L. Jiang, E. D. Ganaa, P. Li, and X.-J. Shen, “Robust feature enhanced deep kernel support vector machine via low rank representation and clustering,” Expert Systems with Applications, vol. 271, p. 126612, May 2025, doi: 10.1016/j.eswa.2025.126612
R. Guido, S. Ferrisi, D. Lofaro, and D. Conforti, “An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review,” Information, vol. 15, no. 4, p. 235, Apr. 2024, doi: 10.3390/info15040235
J. S. Pimentel, R. Ospina, and A. Ara, “A novel fusion Support Vector Machine integrating weak and sphere models for classification challenges with massive data,” Decision Analytics Journal, vol. 11, p. 100457, June 2024, doi: 10.1016/j.dajour.2024.100457
J. Gu and R. G. Congalton, “Assessing the Impact of Mixed Pixel Proportion Training Data on SVM-Based Remote Sensing Classification: A Simulated Study,” Remote Sensing, vol. 17, no. 7, p. 1274, Apr. 2025, doi: 10.3390/rs17071274
J. Nyengere et al., “Forest cover restoration analysis using remote sensing and machine learning in central Malawi,” Trees, Forests and People, vol. 20, p. 100873, June 2025, doi: 10.1016/j.tfp.2025.100873
R. Franco, M. C. Torres-Madronero, M. Casamitjana, and T. Rondon, “Spatial-spectral feature extraction using multispectral linear unmixing for land use land cover change detection,” Journal of South American Earth Sciences, vol. 165, p. 105722, Oct. 2025, doi: 10.1016/j.jsames.2025.105722
Z. Zhu, S. Qiu, and S. Ye, “Remote sensing of land change: A multifaceted perspective,” Remote Sensing of Environment, vol. 282, p. 113266, Dec. 2022, doi: 10.1016/j.rse.2022.113266
T. Wang et al., “An adaptive atmospheric correction algorithm for the effective adjacency effect correction of submeter-scale spatial resolution optical satellite images: Application to a WorldView-3 panchromatic image,” Remote Sensing of Environment, vol. 259, p. 112412, June 2021, doi: 10.1016/j.rse.2021.112412
K. E. Lewińska, S. Ernst, D. Frantz, U. Leser, and P. Hostert, “Global overview of usable Landsat and Sentinel-2 data for 1982–2023,” Data in Brief, vol. 57, p. 111054, Dec. 2024, doi: 10.1016/j.dib.2024.111054
C. Valdivieso-Ros, F. Alonso-Sarria, and F. Gomariz-Castillo, “Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area,” Remote Sensing, vol. 13, no. 9, p. 1770, May 2021, doi: 10.3390/rs13091770
L. A. Brown et al., “GROUNDED EO: Data-driven Sentinel-2 LAI and FAPAR retrieval using Gaussian processes trained with extensive fiducial reference measurements,” Remote Sensing of Environment, vol. 326, p. 114797, Aug. 2025, doi: 10.1016/j.rse.2025.114797
J. Wang et al., “Automatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images,” Remote Sensing of Environment, vol. 264, p. 112604, Oct. 2021, doi: 10.1016/j.rse.2021.112604
N. Wright, J. M. A. Duncan, J. N. Callow, S. E. Thompson, and R. J. George, “CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imagery,” Remote Sensing of Environment, vol. 306, p. 114122, May 2024, doi: 10.1016/j.rse.2024.114122
“Clouds and Image Compositing,” in Cloud-Based Remote Sensing with Google Earth Engine, Cham: Springer International Publishing, 2024, pp. 279–302. doi: 10.1007/978-3-031-26588-4_15
P. A. Palanisamy, J. Zawadzka, K. Jain, S. Bonafoni, and A. Tiwari, “Assessing diurnal land surface temperature variations across landcover and local climate zones: Implications for urban planning and mitigation strategies on socio-economic factors,” Sustainable Cities and Society, vol. 116, p. 105880, Dec. 2024, doi: 10.1016/j.scs.2024.105880
N. Quintero, O. Viedma, S. Veraverbeke, and J. M. Moreno, “Optimising fire severity mapping using pixel-based image compositing,” Remote Sensing of Environment, vol. 321, p. 114687, May 2025, doi: 10.1016/j.rse.2025.114687
E. Barca, M. C. Caputo, and R. Masciale, “Building the optimal hybrid spatial Data-Driven Model: Balancing accuracy and complexity,” International Journal of Applied Earth Observation and Geoinformation, vol. 139, p. 104478, May 2025, doi: 10.1016/j.jag.2025.104478
T. K, R. Kumar, S. Ramadass, and S. Narayanan, “Satellite Remote Sensing Analysis Using Effective Feature Extraction Classification Using Deep Learning Technique.” Springer Science and Business Media LLC, July 22, 2024. doi: 10.21203/rs.3.rs-4634861/v1
S. Amani and H. Shafizadeh-Moghadam, “A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data,” Agricultural Water Management, vol. 284, p. 108324, June 2023, doi: 10.1016/j.agwat.2023.108324
Q. Zhao and Y. Qu, “The Retrieval of Ground NDVI (Normalized Difference Vegetation Index) Data Consistent with Remote-Sensing Observations,” Remote Sensing, vol. 16, no. 7, p. 1212, Mar. 2024, doi: 10.3390/rs16071212
Gessesse , Agenagnew A., “Temporal relationships between time series CHIRPS-rainfall estimation and eMODIS-NDVI satellite images in Amhara Region, Ethiopia,” in Extreme Hydrology and Climate Variability, Elsevier, 2019, pp. 81–92. doi: 10.1016/b978-0-12-815998-9.00008-7
T. R. Tenreiro, M. García-Vila, J. A. Gómez, J. A. Jiménez-Berni, and E. Fereres, “Using NDVI for the assessment of canopy cover in agricultural crops within modelling research,” Computers and Electronics in Agriculture, vol. 182, p. 106038, Mar. 2021, doi: 10.1016/j.compag.2021.106038
S. Samsuddin Sah, K. N. Abdul Maulud, S. Sharil, O. A. Karim, and B. Pradhan, “Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images,” The Egyptian Journal of Remote Sensing and Space Sciences, vol. 26, no. 4, pp. 989–998, Dec. 2023, doi: 10.1016/j.ejrs.2023.11.005
P. P. Patil, M. P. Jagtap, N. Khatri, H. Madan, A. A. Vadduri, and T. Patodia, “Exploration and advancement of NDDI leveraging NDVI and NDWI in Indian semi-arid regions: A remote sensing-based study,” Case Studies in Chemical and Environmental Engineering, vol. 9, p. 100573, June 2024, doi: 10.1016/j.cscee.2023.100573
J. Tian, Y. Tian, Y. Cao, W. Wan, and K. Liu, “Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data,” Sensors, vol. 23, no. 13, p. 5876, June 2023, doi: 10.3390/s23135876
N. Iqbal, R. Mumtaz, U. Shafi, and S. M. H. Zaidi, “Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms,” PeerJ Computer Science, vol. 7, p. e536, May 2021, doi: 10.7717/peerj-cs.536
P. Mohammadpour, D. X. Viegas, A. Pereira, and E. Chuvieco, “Multitemporal Sentinel and GEDI data integration for overstory and understory fuel type classification,” International Journal of Applied Earth Observation and Geoinformation, vol. 139, p. 104455, May 2025, doi: 10.1016/j.jag.2025.104455
N. A. Syam, N. Arifin, W. Firgiawan, and M. F. Rasyid, “Comparison of SVM and Gradient Boosting with PCA for Website Phising Detection,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 2, pp. 691–708, Apr. 2025, doi: 10.52436/1.jutif.2025.6.2.4344
A. Dikshit, B. Pradhan, and M. Santosh, “Artificial neural networks in drought prediction in the 21st century–A scientometric analysis,” Applied Soft Computing, vol. 114, p. 108080, Jan. 2022, doi: 10.1016/j.asoc.2021.108080
T. Qu et al., “A fine crop classification model based on multitemporal Sentinel-2 images,” International Journal of Applied Earth Observation and Geoinformation, vol. 134, p. 104172, Nov. 2024, doi: 10.1016/j.jag.2024.104172
K. Y. Chan et al., “Deep neural networks in the cloud: Review, applications, challenges and research directions,” Neurocomputing, vol. 545, p. 126327, Aug. 2023, doi: 10.1016/j.neucom.2023.126327
E. Hassan, M. Y. Shams, N. A. Hikal, and S. Elmougy, “The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study,” Multimed Tools Appl, vol. 82, no. 11, pp. 16591–16633, May 2023, doi: 10.1007/s11042-022-13820-0
D. Lamani et al., “SVM directed machine learning classifier for human action recognition network,” Scientific Reports, vol. 15, no. 1, Jan. 2025, doi: 10.1038/s41598-024-83529-7
K.-L. Du, B. Jiang, J. Lu, J. Hua, and M. N. S. Swamy, “Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions,” Mathematics, vol. 12, no. 24, p. 3935, Dec. 2024, doi: 10.3390/math12243935
S. F. Hussain, “A novel robust kernel for classifying high-dimensional data using Support Vector Machines,” Expert Systems with Applications, vol. 131, pp. 116–131, Oct. 2019, doi: 10.1016/j.eswa.2019.04.037
J. Huo et al., “Comparison of KAN, MLP, and SVM for NIR Spectra Classification,” in 2024 3rd International Conference on Automation, Robotics and Computer Engineering (ICARCE), China: IEEE, Dec. 2024, pp. 290–294. doi: 10.1109/ICARCE63054.2024.00060
M. -, A. Widodo, M. -, W. F. Mahmudy, and M. M. Arifin, “Comparison of Multi-layer Perceptron and Support Vector Machine Methods on Rainfall Data with Optimal Parameter Tuning,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 7, 2023, doi: 10.14569/IJACSA.2023.0140745
A. Jamali, S. K. Roy, D. Hong, P. M. Atkinson, and P. Ghamisi, “Spatial-Gated Multilayer Perceptron for Land Use and Land Cover Mapping,” IEEE Geosci. Remote Sensing Lett., vol. 21, pp. 1–5, 2024, doi: 10.1109/LGRS.2024.3354175
Md. A. Fattah, S. R. Morshed, and S. Y. Morshed, “Multi-layer perceptron-Markov chain-based artificial neural network for modelling future land-specific carbon emission pattern and its influences on surface temperature,” SN Appl. Sci., vol. 3, no. 3, p. 359, Feb. 2021, doi: 10.1007/s42452-021-04351-8
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anisya, Sumijan, Anna Syahrani
This work is licensed under a Creative Commons Attribution 4.0 International License.
