Remote Sensing Based Land Cover Classification Using Machine Learning and Deep Learning: A Comprehensive Survey
Since the 1990s, remote sensing images have been used for land cover classification combined with Machine
Learning algorithms. The traditional land surveying method only works well in places that are hard to get to, like
high mountain regions, arid and semi-arid land, and densely forested areas. As the satellites and airborne sensors
pass over a specific point of land surface periodically, it is possible to assess the change in land cover over a long
time. With the advent of ML methods, automated land cover classification has been at the center of research
for the last few decades. From 2015 forward, a technical shift has been noticed with the emergence of several
branches of Neural Networks (NN) and Deep Learning (DL). This paper examines current practices, problems,
and trends in satellite image processing. This formal review focused on the summarization of major classification
approaches from 1995. Two dominant research trends have been noticed in automated land cover classification,
e.g., per pixel and subpixel analysis. Classical machine learning algorithms and deep learning methods are mainly
used for per-pixel analysis, whereas Fuzzy algorithms are used for sub-pixel analysis. The current article includes
the research gap in automated land cover classification to provide comprehensive guidance for subsequent research
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Abdi, A. M. 2020. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using sentinel-2 data. GIScience & Remote Sensing 57, 1, 1–20. DOI: https://doi.org/10.1080/15481603.2019.1650447
- Aplin, P. and Atkinson, P. M. 2001. Sub-pixel land cover mapping for per-field classification. DOI: https://doi.org/10.1080/01431160110053176
- International Journal of Remote Sensing 22, 14, 2853–2858.
- Atkinson, P. M. and Tatnall, A. R. 1997. Introduction neural networks in remote sensing. DOI: https://doi.org/10.1080/014311697218700
- International Journal of remote sensing 18, 4, 699–709.
- Bellinger, C., Jabbar, M. S. M., Za¨ıane, O., and Osornio-Vargas, A. 2017. A systematic review of data mining and machine learning for air pollution epidemiology. BMC public health 17, 1, 1–19. DOI: https://doi.org/10.1186/s12889-017-4914-3
- Bischof, H. and Leonardis, A. 1998. Finding optimal neural networks for land use classifi- cation. IEEE transactions on Geoscience and Remote Sensing 36, 1, 337–341. DOI: https://doi.org/10.1109/36.655348
- Borak, J. S. 1999. Feature selection and land cover classification of a modis-like data set for a semiarid environment. International Journal of Remote Sensing 20, 5, 919–938. DOI: https://doi.org/10.1080/014311699212993
- Bovolo, F., Bruzzone, L., and Marconcini, M. 2008. A novel approach to unsupervised change detection based on a semisupervised svm and a similarity measure. IEEE Transac- tions on Geoscience and Remote Sensing 46, 7, 2070–2082. DOI: https://doi.org/10.1109/TGRS.2008.916643
- Brodley, C., Friedl, M., and Strahler, A. 1996. New approaches to classification in remote sensing using homogeneous and hybrid decision trees to map land cover. In IGARSS’96. 1996 International Geoscience and Remote Sensing Symposium. Vol. 1. IEEE, 532–534.
- Brovelli, M. A., Sun, Y., and Yordanov, V. 2020. Monitoring forest change in the amazon using multi-temporal remote sensing data and machine learning classification on google earth engine. ISPRS International Journal of Geo-Information 9, 10, 580. DOI: https://doi.org/10.3390/ijgi9100580
- Bruzzone, L., Chi, M., and Marconcini, M. 2006. A novel transductive svm for semisu- pervised classification of remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing 44, 11, 3363–3373. DOI: https://doi.org/10.1109/TGRS.2006.877950
- Camps-Valls, G. and Bruzzone, L. 2005a. Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43, 6, 1351–1362.
- Camps-Valls, G. and Bruzzone, L. 2005b. Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43, 6, 1351–1362. DOI: https://doi.org/10.1109/TGRS.2005.846154
- Camps-Valls, G., Go´mez-Chova, L., Mun˜oz-Mar´ı, J., Rojo-A´lvarez, J. L., and Mart´ınez-Ramo´n, M. 2008. Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Transactions on Geoscience and Remote Sensing 46, 6, 1822–1835. DOI: https://doi.org/10.1109/TGRS.2008.916201
- Camps-Valls, G., Tuia, D., Bruzzone, L., and Benediktsson, J. A. 2013. Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE signal processing magazine 31, 1, 45–54. DOI: https://doi.org/10.1109/MSP.2013.2279179
- Cao, S., Feng, J., Hu, Z., Li, Q., and Wu, G. 2022. Improving estimation of urban land cover fractions with rigorous spatial endmember modeling. ISPRS Journal of Photogrammetry and Remote Sensing 189, 36–49. DOI: https://doi.org/10.1016/j.isprsjprs.2022.04.019
- Chang, C.-I. 2013. Hyperspectral data processing: algorithm design and analysis. John Wiley & Sons. DOI: https://doi.org/10.1002/9781118269787
- Chen, K., Fu, K., Gao, X., Yan, M., Sun, X., and Zhang, H. 2017. Building extraction from remote sensing images with deep learning in a supervised manner. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 1672–1675. DOI: https://doi.org/10.1109/IGARSS.2017.8127295
- Chen, Y., Lin, Z., Zhao, X., Wang, G., and Gu, Y. 2014. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected topics in applied earth observations and remote sensing 7, 6, 2094–2107. DOI: https://doi.org/10.1109/JSTARS.2014.2329330
- Cheng, G., Yang, C., Yao, X., Guo, L., and Han, J. 2018. When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative cnns. IEEE transactions on geoscience and remote sensing 56, 5, 2811–2821. DOI: https://doi.org/10.1109/TGRS.2017.2783902
- Chi, M. and Bruzzone, L. 2007. Classification of hyperspectral data by continuation semi- supervised svm. In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 3794–3797.
- Civco, D. L. 1993. Artificial neural networks for land-cover classification and mapping. Inter- national journal of geographical information science 7, 2, 173–186. DOI: https://doi.org/10.1080/02693799308901949
- Dixon, B. and Candade, N. 2008. Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? International Journal of Remote Sensing 29, 4, 1185–1206. DOI: https://doi.org/10.1080/01431160701294661
- Fischer, M. M. and Gopal, S. 1996. Fuzzy artmap-a neural classifier for multispectral image classification.
- Foody, G. M. 1996. Approaches for the production and evaluation of fuzzy land cover clas- sifications from remotely-sensed data. International Journal of Remote Sensing 17, 7, 1317–1340. DOI: https://doi.org/10.1080/01431169608948706
- Friedl, M. A., Brodley, C. E., and Strahler, A. H. 1999. Maximizing land cover classifica- tion accuracies produced by decision trees at continental to global scales. IEEE Transactions on Geoscience and Remote Sensing 37, 2, 969–977. DOI: https://doi.org/10.1109/36.752215
- Garg, R., Kumar, A., Prateek, M., Pandey, K., and Kumar, S. 2022. Land cover classification of spaceborne multifrequency sar and optical multispectral data using machine learning. Advances in Space Research 69, 4, 1726–1742. DOI: https://doi.org/10.1016/j.asr.2021.06.028
- Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A., and Hasanlou, M. 2020. Improved land cover map of iran using sentinel imagery within google earth engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing 167, 276–288. DOI: https://doi.org/10.1016/j.isprsjprs.2020.07.013
- Gopal, S. and Woodcock, C. 1996. Remote sensing of forest change using artificial neural networks. IEEE Transactions on Geoscience and Remote Sensing 34, 2, 398–404. DOI: https://doi.org/10.1109/36.485117
- Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Huang, S., Brooks, M., Lee, M. J., and Asadi, H. 2019. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. American Journal of Roentgenology 212, 38–43. DOI: https://doi.org/10.2214/AJR.18.20224
- Huang, F., Yu, Y., and Feng, T. 2019. Automatic extraction of impervious surfaces from high resolution remote sensing images based on deep learning. Journal of Visual Communication and Image Representation 58, 453–461. DOI: https://doi.org/10.1016/j.jvcir.2018.11.041
- Ju, J., Kolaczyk, E. D., and Gopal, S. 2003. Gaussian mixture discriminant analysis and sub- pixel land cover characterization in remote sensing. Remote Sensing of Environment 84, 4, 550–560. DOI: https://doi.org/10.1016/S0034-4257(02)00172-4
- Karpatne, A., Jiang, Z., Vatsavai, R. R., Shekhar, S., and Kumar, V. 2016a. Monitoring land-cover changes: A machine-learning perspective. IEEE Geoscience and Remote Sensing Magazine 4, 2, 8–21.
- Karpatne, A., Jiang, Z., Vatsavai, R. R., Shekhar, S., and Kumar, V. 2016b. Monitoring land-cover changes: A machine-learning perspective. IEEE Geoscience and Remote Sensing Magazine 4, 2, 8–21. DOI: https://doi.org/10.1109/MGRS.2016.2528038
- Keshtkar, H., Voigt, W., and Alizadeh, E. 2017. Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery. Arabian Journal of Geosciences 10, 6, 1–15. DOI: https://doi.org/10.1007/s12517-017-2899-y
- Kussul, N., Lavreniuk, M., Skakun, S., and Shelestov, A. 2017. Deep learning classifica- tion of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters 14, 5, 778–782. DOI: https://doi.org/10.1109/LGRS.2017.2681128
- Li, R., Zheng, S., Duan, C., Wang, L., and Zhang, C. 2022. Land cover classification from remote sensing images based on multi-scale fully convolutional network. Geo-spatial information science, 1–17. DOI: https://doi.org/10.1080/10095020.2021.2017237
- Li, T., Zhang, J., and Zhang, Y. 2014a. Classification of hyperspectral image based on deep belief networks. In 2014 IEEE international conference on image processing (ICIP). IEEE, 5132–5136.
- Li, T., Zhang, J., and Zhang, Y. 2014b. Classification of hyperspectral image based on deep belief networks. In 2014 IEEE international conference on image processing (ICIP). IEEE, 5132–5136. DOI: https://doi.org/10.1109/ICIP.2014.7026039
- Li, W., Fu, H., Yu, L., and Cracknell, A. 2017. Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sensing 9, 1, 22. DOI: https://doi.org/10.3390/rs9010022
- Li, W., Fu, H., Yu, L., Gong, P., Feng, D., Li, C., and Clinton, N. 2016. Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of african land-cover mapping. International journal of remote sensing 37, 23, 5632–5646. DOI: https://doi.org/10.1080/01431161.2016.1246775
- Li, W., Liu, H., Wang, Y., Li, Z., Jia, Y., and Gui, G. 2019. Deep learning-based classifica- tion methods for remote sensing images in urban built-up areas. Ieee Access 7, 36274–36284. DOI: https://doi.org/10.1109/ACCESS.2019.2903127
- Liou, Y.-A., Liu, S.-F., and Wang, W.-J. 2001. Retrieving soil moisture from simulated brightness temperatures by a neural network. IEEE Transactions on Geoscience and Re-
- mote Sensing 39, 8, 1662–1672.
- Liou, Y.-A., Tzeng, Y.-C., and Chen, K.-S. 1999. A neural-network approach to radio- metric sensing of land-surface parameters. IEEE transactions on geoscience and remote sensing 37, 6, 2718–2724. DOI: https://doi.org/10.1109/36.803419
- Liu, W., Seto, K. C., Wu, E. Y., Gopal, S., and Woodcock, C. E. 2004. Art-mmap: A neural network approach to subpixel classification. IEEE transactions on geoscience and remote sensing 42, 9, 1976–1983. DOI: https://doi.org/10.1109/TGRS.2004.831893
- Lu, D. and Weng, Q. 2007. A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing 28, 5, 823– 870. DOI: https://doi.org/10.1080/01431160600746456
- Luus, F. P., Salmon, B. P., Van den Bergh, F., and Maharaj, B. T. J. 2015. Mul- tiview deep learning for land-use classification. IEEE Geoscience and Remote Sensing Letters 12, 12, 2448–2452. DOI: https://doi.org/10.1109/LGRS.2015.2483680
- Lyu, H., Lu, H., and Mou, L. 2016. Learning a transferable change rule from a recurrent neural network for land cover change detection. Remote Sensing 8, 6, 506. DOI: https://doi.org/10.3390/rs8060506
- Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. 2016. Convolutional neural networks for large-scale remote-sensing image classification. IEEE Transactions on geo- science and remote sensing 55, 2, 645–657. DOI: https://doi.org/10.1109/TGRS.2016.2612821
- Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. 2015. Deep super- vised learning for hyperspectral data classification through convolutional neural networks. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 4959–4962. DOI: https://doi.org/10.1109/IGARSS.2015.7326945
- Malhotra, R. 2015. A systematic review of machine learning techniques for software fault prediction. Applied Soft Computing 27, 504–518. DOI: https://doi.org/10.1016/j.asoc.2014.11.023
- Mannan, B., Roy, J., and Ray, A. 1998. Fuzzy artmap supervised classification of multi-spectral remotely-sensed images. International Journal of Remote Sensing 19, 4, 767–774. DOI: https://doi.org/10.1080/014311698215991
- Midhun, M., Nair, S. R., Prabhakar, V. N., and Kumar, S. S. 2014. Deep model for classification of hyperspectral image using restricted boltzmann machine. In Proceedings of DOI: https://doi.org/10.1145/2660859.2660946
- the 2014 international conference on interdisciplinary advances in applied computing. 1–7.
- Miller, D. M., Kaminsky, E. J., and Rana, S. 1995. Neural network classification of remote- sensing data. Computers & Geosciences 21, 3, 377–386. DOI: https://doi.org/10.1016/0098-3004(94)00082-6
- Moody, J. and Darken, C. J. 1989. Fast learning in networks of locally-tuned processing units. Neural computation 1, 2, 281–294. DOI: https://doi.org/10.1162/neco.1918.104.22.1681
- Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirband, S., and Varkonyi-Koczy, A. R. 2019. State of the art of machine learning models in energy systems, a systematic review. Energies 12, 7, 1301. DOI: https://doi.org/10.3390/en12071301
- Nogueira, K., Penatti, O. A., and Dos Santos, J. A. 2017. Towards better exploiting con- volutional neural networks for remote sensing scene classification. Pattern Recognition 61, 539–556. DOI: https://doi.org/10.1016/j.patcog.2016.07.001
- Palaniappan, R., Sundaraj, K., and Ahamed, N. U. 2013. Machine learning in lung sound analysis: a systematic review. Biocybernetics and Biomedical Engineering 33, 3, 129–135. DOI: https://doi.org/10.1016/j.bbe.2013.07.001
- Patra, S., Ghosh, S., and Ghosh, A. 2008. Change detection of remote sensing images with semi-supervised multilayer perceptron. Fundamenta Informaticae 84, 3-4, 429–442.
- Pelletier, C., Webb, G. I., and Petitjean, F. 2019. Temporal convolutional neural network for the classification of satellite image time series. Remote Sensing 11, 5, 523. DOI: https://doi.org/10.3390/rs11050523
- 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, 2411. DOI: https://doi.org/10.3390/rs12152411
- Portugal, I., Alencar, P., and Cowan, D. 2018. The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications 97, 205–227. DOI: https://doi.org/10.1016/j.eswa.2017.12.020
- Powell, R. L., Roberts, D. A., Dennison, P. E., and Hess, L. L. 2007. Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, brazil. DOI: https://doi.org/10.1016/j.rse.2006.09.005
- Remote Sensing of environment 106, 2, 253–267.
- Qian, Y., Zhou, W., Yan, J., Li, W., and Han, L. 2015. Comparing machine learning clas- sifiers for object-based land cover classification using very high resolution imagery. Remote Sensing 7, 1, 153–168. DOI: https://doi.org/10.3390/rs70100153
- Roscher, R., Ro¨mer, C., Waske, B., and Plu¨mer, L. 2015. Landcover classification with self-taught learning on archetypal dictionaries. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2358–2361. DOI: https://doi.org/10.1109/IGARSS.2015.7326282
- Schuman, C. D. and Birdwell, J. D. 2013. Dynamic artificial neural networks with affective systems. PloS one 8, 11, e80455. DOI: https://doi.org/10.1371/journal.pone.0080455
- Scott, G. J., England, M. R., Starms, W. A., Marcum, R. A., and Davis, C. H. 2017a. Training deep convolutional neural networks for land–cover classification of high-resolution imagery. IEEE Geoscience and Remote Sensing Letters 14, 4, 549–553.
- Scott, G. J., England, M. R., Starms, W. A., Marcum, R. A., and Davis, C. H. 2017b. Training deep convolutional neural networks for land–cover classification of high-resolution imagery. IEEE Geoscience and Remote Sensing Letters 14, 4, 549–553. DOI: https://doi.org/10.1109/LGRS.2017.2657778
- Senders, J. T., Staples, P. C., Karhade, A. V., Zaki, M. M., Gormley, W. B., Broek- man, M. L., Smith, T. R., and Arnaout, O. 2018. Machine learning and neurosurgical outcome prediction: a systematic review. World neurosurgery 109, 476–486. DOI: https://doi.org/10.1016/j.wneu.2017.09.149
- Shafizadeh-Moghadam, H., Hagenauer, J., Farajzadeh, M., and Helbich, M. 2015. Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study. International Journal of Geographical Information Science 29, 4, 606–623. DOI: https://doi.org/10.1080/13658816.2014.993989
- Slavkovikj, V., Verstockt, S., De Neve, W., Van Hoecke, S., and Van de Walle, R.
- Hyperspectral image classification with convolutional neural networks. In Proceedings of the 23rd ACM international conference on Multimedia. 1159–1162.
- Spasic, I. and Nenadic, G. 2020. Clinical text data in machine learning: systematic review. DOI: https://doi.org/10.2196/17984
- JMIR medical informatics 8, 3, e17984.
- Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., Rahman, A., et al. 2020. Land-use land-cover classification by machine learning classifiers for satellite ob- servations—a review. Remote Sensing 12, 7, 1135. DOI: https://doi.org/10.3390/rs12071135
- Talukdar, S., Singha, P., Mahato, S., Praveen, B., Rahman, A., et al. 2020. Dynamics of ecosystem services (ess) in response to land use land cover (lu/lc) changes in the lower gangetic plain of india. Ecological Indicators 112, 106121. DOI: https://doi.org/10.1016/j.ecolind.2020.106121
- Thyagharajan, K. and Vignesh, T. 2019. Soft computing techniques for land use and land cover monitoring with multispectral remote sensing images: A review. Archives of Compu- tational Methods in Engineering 26, 2, 275–301. DOI: https://doi.org/10.1007/s11831-017-9239-y
- Tuia, D., Ratle, F., Pacifici, F., Kanevski, M. F., and Emery, W. J. 2009. Active learning methods for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing 47, 7, 2218–2232. DOI: https://doi.org/10.1109/TGRS.2008.2010404
- Tuia, D., Volpi, M., Copa, L., Kanevski, M., and Munoz-Mari, J. 2011. A survey of active learning algorithms for supervised remote sensing image classification. IEEE Journal of Selected Topics in Signal Processing 5, 3, 606–617. DOI: https://doi.org/10.1109/JSTSP.2011.2139193
- Vatsavai, R. R. and Bhaduri, B. 2011. A hybrid classification scheme for mining multisource geospatial data. GeoInformatica 15, 1, 29–47. DOI: https://doi.org/10.1007/s10707-010-0113-4
- Wang, Z., Zou, C., and Cai, W. 2020. Small sample classification of hyperspectral remote sensing images based on sequential joint deeping learning model. IEEE Access 8, 71353– 71363. DOI: https://doi.org/10.1109/ACCESS.2020.2986267
- Wu, L., Zhu, X., Lawes, R., Dunkerley, D., and Zhang, H. 2019. Comparison of machine learning algorithms for classification of lidar points for characterization of canola canopy structure. International Journal of Remote Sensing 40, 15, 5973–5991. DOI: https://doi.org/10.1080/01431161.2019.1584929
- Xu, X., Li, W., Ran, Q., Du, Q., Gao, L., and Zhang, B. 2017. Multisource remote sensing data classification based on convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing 56, 2, 937–949. DOI: https://doi.org/10.1109/TGRS.2017.2756851
- Yilmaz, I. and Kaynar, O. 2011. Multiple regression, ann (rbf, mlp) and anfis models for prediction of swell potential of clayey soils. Expert systems with applications 38, 5, 5958– 5966. DOI: https://doi.org/10.1016/j.eswa.2010.11.027
- Zhang, C., Yue, P., Tapete, D., Shangguan, B., Wang, M., and Wu, Z. 2020. A multi- level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images. International Journal of Applied Earth Observation and Geoinformation 88, 102086. DOI: https://doi.org/10.1016/j.jag.2020.102086