Classification of Sentinel 2 Images using Customized Convolution Neural Networks

Authors

  • Geetha M. 20231Department of MCA, BIET, Davanagere - 577004, INDIA https://orcid.org/0000-0002-8762-980X
  • Asha Gowda Karegowda Department of MCA, SIT, Tumakuru - 572103, INDIA
  • Nandeesha Department of Civil Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, INDIA,
  • Nagaraj B. V. Department of Management Studies, KIAMS, Harihara, Karnataka, INDIA

Keywords:

Convolution Neural Network, Deep Learning, Remote Sensing, Sentinel 2

Abstract

With the development of Convolutional Neural Networks and increased processing power in recent years, the discipline of deep learning and machine learning has made significant advancements. One of the most important networks in the deep learning space is the Convolutional Neural Network. In computer vision and natural language processing, convolutional neural networks have achieved remarkable successes. Based on the land usage and land cover of the specific area, satellite images are useful to constantly monitor. Classifying satellite images using cutting-edge deep learning is one of the potential and difficult tasks of remote sensing. Three of the most popular Convolutional Network models viz., Custom Architecture, VGG16, and Resnet34, were utilised for classification in order to assess and investigate deep learning convolutional models utilising satellite data. The multispectral Sentinel 2 image with its 13 spectral bands served as the training data for these three models. The dataset of the study area was created manually and the featured images were classified into six classes. The accuracy for VGG16 was found to be 90.70% and that using Resnet34 and custom architecture was respectively 91.50% and 93.73%, thus demonstrating the fact that Custom architecture produces more accurate results than the other two transfer learning techniques.

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References

H.Yassine1, K. Tout , and M. Jaber "Improving LULC Classification From Satellite Imagery Using Deep Learning - Eurosat Dataset", The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B3-2021 XXIV ISPRS Congress (2021 edition).

Basha, U.I., Suresh, U., Raju, G.S., Rajasekhar, M., Veeraswamy, G. and Balaji, E., "Land Use and Land Cover Analysis Using Remote Sensing and GIS: A Case Study in Somavathi River, Anantapur District, Andhra Pradesh, India", Nature Environment & Pollution Technology, 17(3), pp.1029-1033, 2018.

Azzari, G. and Lobell, D.B., "Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring", Remote Sensing of Environment, 202, pp.64-74, 2017.

Zhang, H.K. and Roy, D.P., "Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification", Remote Sensing of Environment, 197, pp.15-34.

Turner, B.L., Lambin, E.F. and Reenberg, A., "The emergence of land change science for global environmental change and sustainability, Proceedings of the National Academy of Sciences, 104(52), pp.20666- 20671, 2017.

Goldblatt, R., Stuhlmacher, M.F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano- Candela, F.,

Khandelwal, A.K., Cheng, W.H. and Balling, R.C" Using Landsat and night time lights for supervised pixel-based image classification of urban land cover", Remote Sensing of Environment, 205, pp.253-27, 2018.

Gharbia, R., Hassanien, A.E., El-Baz, A.H., Elhoseny, M. and Gunasekaran, M., "Multi-spectral and panchromatic image fusion approach using stationary wavelet transform and swarm flower pollination optimization for remote sensing applications”, Future Generation Computer Systems, 2018.

Patel, N.N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F.R., Tatem, A.J. and Trianni, G.,"Multi temporal settlement and population mapping from Landsat using Google Earth Engine", International Journal of Applied Earth Observation and Geo information, 35, pp.199-208, 2015,

Gharbia, R., El-Baz, A.H. and Hassanien, A.E., “International Conference on Computing, Communication, and Automation (ICCCA), pp. 1080-1085. IEEE, 2016.

Ban, Y., Jacob, A. and Gamba, P, "Space borne SAR data for global urban mapping at 30 m resolution using a robust urban extractor", ISPRS Journal of Photogrammetry and Remote Sensing, 103, pp.28-37, , 2015.

Wu, S.S., Qiu, X., Usery, E.L. and Wang, L, "Using geometrical, textural, and contextual information of land parcels for the classification of detailed urban land use", Annals of the Association of American Geographers, 99(1), pp.76-98, 2009.

Schmidhuber, J., "Deep learning in neural networks: An overview”, Neural networks, 61, pp.85-117.

Wu, X., Sahoo, D. and Hoi, S.C., "Recent advances in deep learning for object detection", Neuro computing, 2020. 2015

He, K., Zhang, X., Ren, S. and Sun, J.,"Deep residual learning for image recognition", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 10. 2016.

Abdi, A.M., "Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data", GIS Science& Remote Sensing, 57(1), pp.1-20, 2020.

Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J. and Atkinson, P.M., "Joint Deep Learning for land cover and land use classification", Remote sensing of environment, 221, pp.173-187, 2019.

Simonyan, K. and Zisserman, A., "Very deep convolutional networks for large scale image recognition",.arXiv preprint arXiv:1409.1556, 2014.

Reham Gharbia Nour Eldeen M. Khalifa , and Aboul Ella Hassanien "Land Cover Classification using Deep Convolutional Neural Networks, Chapter · June 2021 DOI: 10.1007/978-3-030-71187-0_84.

M. Voelsen1,∗ , J. Bostelmann , A. Maas , F. Rottensteiner , C. Heipke1 “Automatically Generated Training Data For Land Cover Classification With CNNs Using Sentinel-2 Images”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B3-2020, 2020 XXIV ISPRS Congress (2020 edition).

Kavita Bhosle and Vijaya Musande, “Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images”, Journal of the Indian Society of Remote Sensing, 2019, https://doi.org/10.1007/s12524-019-01041-2

Acquisition of Sentinel 2

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Published

16.01.2023

How to Cite

[1]
Geetha M., A. G. . Karegowda, Nandeesha, and Nagaraj B. V., “Classification of Sentinel 2 Images using Customized Convolution Neural Networks”, Int J Intell Syst Appl Eng, vol. 11, no. 1s, pp. 136–142, Jan. 2023.