Classification of Remote Sensing Dataset Imagery Using Deep Learning Techniques
DOI:
https://doi.org/10.29304/jqcsm.2025.17.11979Keywords:
Deep learning, Hyperspectral, Multispectral, Satellite imagery, Remote SensingAbstract
Integrating deep learning with remote sensing is contentious due to the disparity between natural and remote sensing images, raising the question of whether deep learning techniques can transform the remote sensing domain. This study covers prior research employing deep learning methodologies for land use and land cover categorization in the last five years. Our study is categorized into three groups based on the data type utilized in each analysis. The initial group comprised roughly 20 prior studies of multispectral data, the subsequent group contained 8 studies of hyperspectral data, and the final group encompassed 10 studies of aerial photos obtained via drones or radar. Each data type is physically distinct and serves a specific purpose. Furthermore, we chose one of the studies and implemented it with the specified data to categorize land cover with the CNN technique, employing a 4-band CNN patch size of 5x5. An accuracy of 95.0% was achieved, whereas a 4-band CNN with a patch size of 9x9 attained an accuracy of 91.5% on the training data for a region in Pakistan. The accuracy for the test data in Lahore City was 93.6% for the 4-band CNN with a patch size of 5x5 and 91.1% for the 4-band CNN with a patch size of 9x9.
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References
Ma, L., et al., Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 2019. 152: p. 166-177.
E. D. Chaves, M., M. C. A. Picoli, and I. D. Sanches, Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sensing, 2020. 12(18).
Tavares, P.A., et al., Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belem, Eastern Brazilian Amazon. Sensors (Basel), 2019. 19(5).
Zheng, H., et al., Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features. Remote Sensing, 2017. 9(12).
Khlifi, M.K., W. Boulila, and I.R. Farah, Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications — A comprehensive review. Computer Science Review, 2023. 50.
Akosah, S., et al., Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review. Remote Sensing, 2024. 16(16).
Ali, K. and B.A. Johnson, Land-Use and Land-Cover Classification in Semi-Arid Areas from Medium-Resolution Remote-Sensing Imagery: A Deep Learning Approach. Sensors (Basel), 2022. 22(22).
Rajaneesh, A., et al., Monitoring and Mapping of Shallow Landslides in a Tropical Environment Using Persistent Scatterer Interferometry: A Case Study from the Western Ghats, India. Geomatics, 2020. 1(1): p. 3-17.
Moskolaï, W.R., et al., A Workflow for Collecting and Preprocessing Sentinel-1 Images for Time Series Prediction Suitable for Deep Learning Algorithms. Geomatics, 2022. 2(4): p. 435-456.
Morgan, G.R., et al., Deep Learning for Urban Tree Canopy Coverage Analysis: A Comparison and Case Study. Geomatics, 2024. 4(4): p. 412-432.
Tombe, R. and S. Viriri, Remote Sensing Image Scene Classification: Advances and Open Challenges. Geomatics, 2023. 3(1): p. 137-155.
Kavhu, B., Z.E. Mashimbye, and L. Luvuno, Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory. Geomatics, 2022. 2(4): p. 518-539.
Cheng, X., et al., Application of Deep Learning in Multitemporal Remote Sensing Image Classification. Remote Sensing, 2023. 15(15).
Turkoglu, M.O., et al., Crop mapping from image time series: Deep learning with multi-scale label hierarchies. Remote Sensing of Environment, 2021. 264.
Teixeira, I., et al., Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review. Agriculture, 2023. 13(5).
Kharroubi, A., et al., Three Dimensional Change Detection Using Point Clouds: A Review. Geomatics, 2022. 2(4): p. 457-485.
Lunga, D., et al., ReSFlow: A Remote Sensing Imagery Data-Flow for Improved Model Generalization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021. 14: p. 10468-10483.
Shafique, A., et al., Deep Learning-Based Change Detection in Remote Sensing Images: A Review. Remote Sensing, 2022. 14(4).
Al-Zubaidi, E.A., A.H. Al-Sulttani, and F. Rabee, Sand Dunes Spectral Index Determination Using Machine Learning Model: Case study of Baiji Sand Dunes Field Northern Iraq. Iraqi Geological Journal, 2022. 55(1F): p. 102-121.
Al-Zubaidi, E.A., F. Rabee, and A.H. Al-Sulttani, Calculating Spectral Index Based on Linear SVM Methods for Landsat OLI: Baiji Sand Dunes a Case Study, Iraq. International Journal of Computing and Digital Systems, 2023. 13(1): p. 1-15.
Bai, Y., et al., Deep learning in different remote sensing image categories and applications: status and prospects. International Journal of Remote Sensing, 2022. 43(5): p. 1800-1847.
Laban, N., et al., Multiscale Satellite Image Classification Using Deep Learning Approach, in Machine Learning and Data Mining in Aerospace Technology. 2020. p. 165-186.
Shafaey, M.A., et al., Deep Learning for Satellite Image Classification, in Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. 2019. p. 383-391.
Patel, A., et al., Transfer Learning for LiDAR-Based Lane Marking Detection and Intensity Profile Generation. Geomatics, 2021. 1(2): p. 287-309.
Pelletier, C., G. Webb, and F. Petitjean, Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series. Remote Sensing, 2019. 11(5).
Kyriou, A., et al., Review of Remote Sensing Approaches and Soft Computing for Infrastructure Monitoring. Geomatics, 2023. 3(3): p. 367-394.
Ott, S., et al., Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds. Geomatics, 2023. 3(4): p. 501-521.
Chu, Y., et al., A review of distributed solar forecasting with remote sensing and deep learning. Renewable and Sustainable Energy Reviews, 2024. 198.
Jozdani, S.E., B.A. Johnson, and D. Chen, Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification. Remote Sensing, 2019. 11(14).
Stoian, A., et al., Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems. Remote Sensing, 2019. 11(17).
Marjani, M., M. Mahdianpari, and F. Mohammadimanesh, CNN-BiLSTM: A Novel Deep Learning Model for Near-Real-Time Daily Wildfire Spread Prediction. Remote Sensing, 2024. 16(8).
Gharbia, R., Deep Learning for Automatic Extraction of Water Bodies Using Satellite Imagery. Journal of the Indian Society of Remote Sensing, 2023. 51(7): p. 1511-1521.
Azedou, A., et al., Enhancing Land Cover/Land Use (LCLU) classification through a comparative analysis of hyperparameters optimization approaches for deep neural network (DNN). Ecological Informatics, 2023. 78.
Aljebreen, M., et al., Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images. IEEE Access, 2024. 12: p. 11147-11156.
Qianxiong Xu, et al . Road Extraction with Satellite Images and Partial Road Maps JOURNAL OF LATEX CLASS FILES, VOL. 18, NO. 9, SEPTEMBER 2020.
Ioannis, P., et al., Benchmarking and scaling of deep learning models.. arXiv:2111.09451v3 [cs.CV] 14 Sep 202219..
Rousset, G., et al., Assessment of Deep Learning Techniques for Land Use Land Cover Classification in Southern New Caledonia. Remote Sensing, 2021. 13(12).
Hasan, S.F., M.A. Shareef, and N.D. Hassan, Speckle filtering impact on land use/land cover classification area using the combination of Sentinel-1A and Sentinel-2B (a case study of Kirkuk city, Iraq). Arabian Journal of Geosciences, 2021. 14(4).
Zhang, K., et al., Remote Sensing Image Land Classification Based on Deep Learning. Scientific Programming, 2021. 2021: p. 1-12.
Yuan, K., et al., Deep-Learning-Based Multispectral Satellite Image Segmentation for Water Body Detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021. 14: p. 7422-7434.
Yao, X., et al., Land Use Classification of the Deep Convolutional Neural Network Method Reducing the Loss of Spatial Features. Sensors (Basel), 2019. 19(12).
Song, H., Y. Kim, and Y. Kim, A Patch-Based Light Convolutional Neural Network for Land-Cover Mapping Using Landsat-8 Images. Remote Sensing, 2019. 11(2).
Gaetano, R., et al., A Two-Branch CNN Architecture for Land Cover Classification of PAN and MS Imagery. Remote Sensing, 2018. 10(11).
Hu, Y., et al., A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China. Remote Sensing, 2018. 10(12).
Qiu, C., et al., Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network. ISPRS J Photogramm Remote Sens, 2019. 154: p. 151-162.
Sharma, A., et al., A patch-based convolutional neural network for remote sensing image classification. Neural Netw, 2017. 95: p. 19-28.
Kim, T.-H., et al., Application of Hyperspectral Image for Monitoring in Coastal Area with Deep Learning: A Case Study of Green Algae on Artificial Structure. Journal of Marine Science and Engineering, 2024. 12(11).
Asghari Beirami, B., M. Alizadeh Pirbasti, and V. Akbari, SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images. Applied Sciences, 2024. 14(16).
Cheng, H., et al., A communication-efficient distributed deep learning remote sensing image change detection framework. International Journal of Applied Earth Observation and Geoinformation, 2024. 129.
Yue, J., et al., Analyzing winter-wheat biochemical traits using hyperspectral remote sensing and deep learning. Computers and Electronics in Agriculture, 2024. 222.
Liu, J., et al., Hyperspectral Remote Sensing Images Feature Extraction Based on Spectral Fractional Differentiation. Remote Sensing, 2023. 15(11).
Xia, B., et al., Land Resource Use Classification Using Deep Learning in Ecological Remote Sensing Images. Comput Intell Neurosci, 2022. 2022: p. 7179477.
Feng, Q., et al., Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network. ISPRS International Journal of Geo-Information, 2019. 8(1).
Song, A., et al., Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks. Remote Sensing, 2018. 10(11).
Jia, P., et al., Semantic segmentation of deep learning remote sensing images based on band combination principle: Application in urban planning and land use. Computer Communications, 2024. 217: p. 97-106.
Fayaz, M., et al., Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery. Applied Sciences, 2024. 14(5).
Li, N., et al., Automatic labelling framework for optical remote sensing object detection samples in a wide area using deep learning. Expert Systems with Applications, 2024. 255.
Wambugu, N., et al., A hybrid deep convolutional neural network for accurate land cover classification. International Journal of Applied Earth Observation and Geoinformation, 2021. 103.
Al-Najjar, H.A.H., et al., Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks. Remote Sensing, 2019. 11(12).
Zhu, Y., et al., Multitemporal Relearning With Convolutional LSTM Models for Land Use Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021. 14: p. 3251-3265.
Anuar, M.M., et al., Aerial Imagery Paddy Seedlings Inspection Using Deep Learning. Remote Sensing, 2022. 14(2).
Ķēniņš, R., Land Cover Classification using Very High Spatial Resolution Remote Sensing Data and Deep Learning. Latvian Journal of Physics and Technical Sciences, 2020. 57(1-2): p. 71-77.
Liu, C., et al., Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network. Remote Sensing, 2020. 12(2).
Carranza-García, M., J. García-Gutiérrez, and J.C. Riquelme, A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks. Remote Sensing, 2019. 11(3).
; Available from: https://plugins.qgis.org/plugins/produce_training_data_for_deep_learning/#plugin-versions.
Tripathy, P. Is CNN equally shiny on mid-resolution satellite data? 2020; Available from: https://towardsdatascience.com/is-cnn-equally-shiny-on-mid-resolution-satellite-data-9e24e68f0c08.
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Copyright (c) 2025 Zainab Nasser, Ehsan Ali Al-Zubaidi, Ehsan Ali Al-Zubaidi
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