MULTI-SEASONAL LAND COVER CLASSIFICATION THROUGH INTEGRATION OF SENTINEL-1 AND 2 DATA WITH SERIAL CELLS FROM LSTM: KARBALA CASE STUDY

Safaa Hadi  Kother; Hadab Khalid  Obayes

doi:10.30572/2018/KJE/170217

Authors

Safaa Hadi Kother College of Information Technology, University of Babylon, Babylon, Iraq
Hadab Khalid Obayes College of Education for Humanities Studies, University of Babylon, Babylon, Iraq

DOI:

https://doi.org/10.30572/2018/KJE/170217

Keywords:

Remote Sensing (RS), Land Cover (LC) Classification, Sentinel-1 (S1), Sentinel-2 (S2), Long Short-Term Memory (LSTM), Zonal Statistics (ZS), Time series data

Abstract

With significant technological advancements, deep learning models have become powerful and essential tools for image classification and object recognition, particularly in classifying land cover images captured by satellite sensors. However, these models require a substantial amount of high-quality training data. This study introduces a novel strategy for enhancing and increasing the size of the time series of training samples. Zonal statistics are used to eliminate class contamination within the buffer area. This research aims to utilize time series data and select suitable bands to improve classification accuracy through multiple Long Short-Term Memory (LSTM) cells. By integrating data from Sentinel-1 (S1) and Sentinel-2 (S2) sensors, the study provides comprehensive insights for the research area of Karbala City, Iraq. The results demonstrated that the proposed system achieved high accuracy compared to prior studies, the overall accuracy reaching 98.66% and 96.75% for the Kappa coefficient by using zonal statistics

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References

Acharya, S., Hori, T. and Karki, S. (2023) ‘Assessing the spatio-temporal impact of landuse landcover change on water yield dynamics of rapidly urbanizing Kathmandu valley watershed of Nepal’, Journal of Hydrology: Regional Studies, 50, p. 101562. Available at: https://doi.org/10.1016/j.ejrh.2023.101562.

Almeida, C.A. de, Coutinho, A.C., Esquerdo, J.C. dalla M., Adami, M., Venturieri, A., Diniz, C.G., Dessay, N., Durieux, L. and Gomes, A.R. (2016) ‘High spatial resolution land use and land cover mapping of the Brazilian legal Amazon in 2008 using Landsat-5/TM and MODIS data’, Acta Amazonica, pp. 291–302. Available at: https://doi.org/10.1590/1809-4392201505504.

Alsraratee, Abdullah and Al-Azawei, A. (2025) ‘CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES’, Kufa Journal of Engineering, 16(2), pp. 96–118. Available at: https://doi.org/10.30572/2018/KJE/160206.

Anokye, M., Cui, X., Yang, F., Wang, P., Sun, Y., Ma, H. and Amoako, E. (2023) ‘Optimizing multi-classifier fusion for seabed sediment classification using machine learning’, International Journal of Digital Earth, 17. Available at: https://doi.org/10.1080/17538947.2023.2295988.

Ashraf, T., Yin, F., Liu, L. and Zhang, Q. (2024) ‘Land Subsidence Detection Using SBAS- and Stacking-InSAR with Zonal Statistics and Topographic Correlations in Lakhra Coal Mines, Pakistan’, Remote Sensing, 16(20), p. 3815. Available at: https://doi.org/10.3390/rs16203815.

Bao, Y., Lin, L., Wu, S., Kwal Deng, K.A. and Petropoulos, G.P. (2018) ‘Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model’, International Journal of Applied Earth Observation and Geoinformation, 72, pp. 76–85. Available at: https://doi.org/https://doi.org/10.1016/j.jag.2018.05.026.

Choudhury, U., Singh, S.K., Kumar, A., Meraj, G., Kumar, P. and Kanga, S. (2023) ‘Assessing Land Use/Land Cover Changes and Urban Heat Island Intensification: A Case Study of Kamrup Metropolitan District, Northeast India (2000–2032)’, Earth (Switzerland), 4(3), pp. 503–521. Available at: https://doi.org/10.3390/earth4030026.

Colson, D., Petropoulos, G.P. and Ferentinos, K.P. (2018) ‘Exploring the Potential of Sentinels-1 & 2 of the Copernicus Mission in Support of Rapid and Cost-effective Wildfire Assessment’, International Journal of Applied Earth Observation and Geoinformation, 73, pp. 262–276. Available at: https://doi.org/https://doi.org/10.1016/j.jag.2018.06.011.

Dagne, S.S., Hirpha, H.H., Tekoye, A.T., Dessie, Y.B. and Endeshaw, A.A. (2023) ‘Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia’, Environmental Systems Research, 12(1), p. 40. Available at: https://doi.org/10.1186/s40068-023-00324-5.

Erinjery, J.J., Singh, M. and Kent, R. (2018) ‘Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery’, Remote Sensing of Environment, 216, pp. 345–354. Available at: https://doi.org/https://doi.org/10.1016/j.rse.2018.07.006.

H, S. (2024) ‘CLOUD-SMART SURVEILLANCE: ENHANCING ANOMALY DETECTION IN VIDEO STREAMS WITH DF-CONVLSTM-BASED VAE-GAN’, Kufa Journal of Engineering, 15(4), pp. 125–140. Available at: https://doi.org/10.30572/2018/KJE/150409.

Hu, B., Xu, Y., Wan, B., Wu, X. and Yi, G. (2018) ‘Hydrothermally altered mineral mapping using synthetic application of Sentinel-2A MSI, ASTER and Hyperion data in the Duolong area, Tibetan Plateau, China’, Ore Geology Reviews, 101, pp. 384–397. Available at: https://doi.org/https://doi.org/10.1016/j.oregeorev.2018.07.017.

Ienco, D., Gaetano, R., Dupaquier, C. and Maurel, P. (2017) ‘Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks’, IEEE Geoscience and Remote Sensing Letters, 14(10), pp. 1685–1689. Available at: https://doi.org/10.1109/LGRS.2017.2728698.

Kother, S.H. and Obayes, H.K. (2025) ‘Impact of Bands Number on LULC Classification Accuracy in Sentinel-2 Images BT - New Trends in Information and Communications Technology Applications’, in A.M. Al-Bakry, M.A. Sahib, J.A. Aldhaibani, and A.N. Al-Shuwaili (eds). Cham: Springer Nature Switzerland, pp. 97–115.

Lunetta, R.S., Knight, J.F., Ediriwickrema, J., Lyon, J.G. and Worthy, L.D. (2006) ‘Land-cover change detection using multi-temporal MODIS NDVI data’, Remote Sensing of Environment, 105(2), pp. 142–154. Available at: https://doi.org/https://doi.org/10.1016/j.rse.2006.06.018.

Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S. and Gill, E. (2019) ‘The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform’, Remote Sensing, 11(1). Available at: https://doi.org/10.3390/rs11010043.

McFEETERS, S.K. (1996) ‘The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features’, International Journal of Remote Sensing, 17(7), pp. 1425–1432. Available at: https://doi.org/10.1080/01431169608948714.

Mohammed, H., Kareem, S. and Mohammed, A. (2022) ‘A COMPARATIVE EVALUATION OF DEEP LEARNING METHODS IN DIGITAL IMAGE CLASSIFICATION’, Kufa Journal of Engineering, 13(4), pp. 53–69. Available at: https://doi.org/10.30572/2018/KJE/130405.

Nigar, A., Li, Y., Jat Baloch, M.Y., Alrefaei, A.F. and Almutairi, M.H. (2024) ‘Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications’, Frontiers in Environmental Science, 12. Available at: https://doi.org/10.3389/fenvs.2024.1378443.

Phan, T.N., Kuch, V. and Lehnert, L.W. (2020) ‘Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition’, Remote Sensing, 12(15). Available at: https://doi.org/10.3390/rs12152411.

Rajendran, G.B., Kumarasamy, U.M., Zarro, C., Divakarachari, P.B. and Ullo, S.L. (2020) ‘Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images’, Remote Sensing, 12(24), p. 4135. Available at: https://doi.org/10.3390/rs12244135.

Roteta, E., Bastarrika, A., Padilla, M., Storm, T. and Chuvieco, E. (2019) ‘Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa’, Remote Sensing of Environment, 222, pp. 1–17. Available at: https://doi.org/10.1016/j.rse.2018.12.011.

Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. and others (1974) ‘Monitoring vegetation systems in the Great Plains with ERTS’, NASA Spec. Publ, 351(1), p. 309.

Ruzza, G., Guerriero, L., Grelle, G., Guadagno, F.M. and Revellino, P. (2019) ‘Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery’, Water, 11(11). Available at: https://doi.org/10.3390/w11112289.

Sannigrahi, S., Bhatt, S., Rahmat, S., Paul, S.K. and Sen, S. (2018) ‘Estimating global ecosystem service values and its response to land surface dynamics during 1995–2015’, Journal of Environmental Management, 223, pp. 115–131. Available at: https://doi.org/https://doi.org/10.1016/j.jenvman.2018.05.091.

Santos, L.A., Ferreira, K.R., Camara, G., Picoli, M.C.A. and Simoes, R.E. (2021) ‘Quality control and class noise reduction of satellite image time series’, ISPRS Journal of Photogrammetry and Remote Sensing, 177, pp. 75–88. Available at: https://doi.org/https://doi.org/10.1016/j.isprsjprs.2021.04.014.

Shorten, C. and Khoshgoftaar, T.M. (2019) ‘A survey on Image Data Augmentation for Deep Learning’, Journal of Big Data, 6(1), p. 60. Available at: https://doi.org/10.1186/s40537-019-0197-0.

Tejasree, G. and Agilandeeswari, L. (2024) ‘Land use/land cover (LULC) classification using deep-LSTM for hyperspectral images’, The Egyptian Journal of Remote Sensing and Space Sciences, 27(1), pp. 52–68. Available at: https://doi.org/https://doi.org/10.1016/j.ejrs.2024.01.004.

Wang, S., Ma, Q., Ding, H. and Liang, H. (2018) ‘Detection of urban expansion and land surface temperature change using multi-temporal landsat images’, Resources, Conservation and Recycling, 128, pp. 526–534. Available at: https://doi.org/https://doi.org/10.1016/j.resconrec.2016.05.011.

Winsemius, S. and Braaten, J. (2024) ‘Zonal Statistics’, Cloud-Based Remote Sensing with Google Earth Engine, pp. 463–485. Available at: https://doi.org/10.1007/978-3-031-26588-4_24.

Y. Zha, J.G. and Ni, S. (2003) ‘Use of normalized difference built-up index in automatically mapping urban areas from TM imagery’, International Journal of Remote Sensing, 24(3), pp. 583–594. Available at: https://doi.org/10.1080/01431160304987.

Yassine, H., Tout, K. and Jaber, M. (2021) ‘IMPROVING LULC CLASSIFICATION FROM SATELLITE IMAGERY USING DEEP LEARNING – EUROSAT DATASET’, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2, pp. 369–376. Available at: https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-369-2021.

Zhang, H., Li, J., Wang, T., Lin, H., Zheng, Z., Li, Y. and Lu, Y. (2018) ‘A manifold learning approach to urban land cover classification with optical and radar data’, Landscape and Urban Planning, 172, pp. 11–24. Available at: https://doi.org/https://doi.org/10.1016/j.landurbplan.2017.12.009.

Zurqani, H., Albukhari, A., Al Daikh, A.S., Elfadli, K. and Bataw, A. (2022) ‘Geospatial Mapping and Analysis of the 2019 Flood Disaster Extent and Impact in the City of Ghat in Southwestern Libya Using Google Earth Engine and Deep Learning Technique’, in. Available at: https://doi.org/10.1007/978-3-030-97810-5_10.