Deep learning architectures for land cover classification using red and near-infrared satellite images

  • Anju Unnikrishnan
  • V. SowmyaEmail author
  • K. P. Soman


Classification of remotely sensed data requires the modelling of suitable image processing algorithms. The rise of machine learning systems upgraded the viability of satellite image applications. Using Convolutional Neural Networks (CNN), benchmark classification exactness can be accomplished for land cover grouping. Motivated by the concept of Normalized Difference Vegetation Index (NDVI), this paper utilizes only the red and near infrared (NIR) band information for classifying the publicly available SAT-4 and SAT-6 datasets. This is done, since NDVI computation requires only the two band (red and NIR) information and the classes involved in the dataset are types of vegetation. In this work, new deep learning architectures for three different networks (AlexNet, ConvNet, VGG) were proposed by hypertuning the network and the input as two band data. The modified architectures with the two band information along with reduced number of filters were trained and tested model manages to classify the images into different classes. The proposed architectures are compared against the existing architectures in terms of accuracy, precision and trainable parameters. The proposed architecture is found to perform equally efficient by retaining high accuracy with less number of trainable parameters, when compared against the the performance of benchmark deep learning architectures for satellite image classification.


Satellite image classification SAT-4 SAT-6 Landcover Trainable parameters Normalized difference vegetation index Image processing 



  1. 1.
    Audebert N, Le Saux B, Lefèvre S (2017) Segment-before-detect: vehicle detection and classification through semantic segmentation of aerial images. Remote Sens 9(4):368CrossRefGoogle Scholar
  2. 2.
    Basu S, Ganguly S, Mukhopadhyay S, DiBiano R, Karki M, Nemani R (2015) Deepsat: a learning framework for satellite imagery. In: Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems, p 37Google Scholar
  3. 3.
    Bragilevsky L, Bajić IV (2017) Deep learning for Amazon satellite image analysis. Commun Comput Signal Process (PACRIM), 1–5Google Scholar
  4. 4.
    Chen H, Wang Y, Gao S (2017) Assessing relationship of air quality index and vegetation type using hyperspectral remote sensing. In: Geoscience and remote sensing symposium (IGARSS), pp 4878–4881Google Scholar
  5. 5.
    Chippy J, Jacob NV, Renu RK, Sowmya V, Soman K (2017) Least square denoising in spectral domain for hyperspectral images. Procedia Comput Sci 115:399–406CrossRefGoogle Scholar
  6. 6.
    Dahigamuwa T, Yu Q, Gunaratne M (2016) Feasibility study of land cover classification based on normalized difference vegetation index for landslide risk assessment. Geosciences 6(4):45CrossRefGoogle Scholar
  7. 7.
    Dev S, Wen B, Lee YH, Winkler S (2016) Ground-based image analysis: a tutorial on machine-learning techniques and applications. IEEE Geosci Remote Sens Mag, 79–93Google Scholar
  8. 8.
    Dixon K, Deepa M, Ajay A, Sowmya V, Soman KP (2016) Aerial and satellite image denoising using least square weighted regularization method. Ind J Sci Technol 9(30):1–10Google Scholar
  9. 9.
    Dutta S, Manideep BC, Rai S, Vijayarajan V (2017) A comparative study of deep learning models for medical image classification. IOP Conf Series: Mater Sci Eng 263(4):042097CrossRefGoogle Scholar
  10. 10.
    Haridas N, Aswathy C, Sowmya V, Soman K (2016) Hyperspectral image denoising using legendre-fenchel transform for improved sparsity based classification. Intell Syst Technol Appl, 521–528Google Scholar
  11. 11.
    Jeevalakshmi D, Reddy SN, Manikiam B (2016) Land cover classification based on NDVI using LANDSAT8 time series: a case study Tirupati region. Commun Signal Process (ICCSP), 1332–1335Google Scholar
  12. 12.
    Kaiser P, Wegner JD, Lucchi A, Jaggi M, Hofmann T, Schindler K (2017) Learning aerial image segmentation from online maps. IEEE Trans Geosci Remote Sens 55(11):6054–6068CrossRefGoogle Scholar
  13. 13.
    Li H, Tao C, Wu Z, Chen J, Gong J, Deng M (2017) RSI-CB: a large scale remote sensing image classification benchmark via crowdsource data. arXiv:1705.10450
  14. 14.
    Liu Q, Hang R, Song H, Li Z (2018) Learning multiscale deep features for high-resolution satellite image scene classification. IEEE Trans Geosci Remote Sens 56 (1):117–26CrossRefGoogle Scholar
  15. 15.
    Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870CrossRefGoogle Scholar
  16. 16.
    Lunga D, Yang HL, Reith A, Weaver J, Yuan J, Bhaduri B (2018) Domain-adapted convolutional networks for satellite image classification: a large-scale interactive learning workflow. IEEE J Selected Topics Appl Earth Observ Remote Sens 11(3):962–77CrossRefGoogle Scholar
  17. 17.
    Makantasis K, Karantzalos K, Doulamis A, Doulamis N (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. Geosci Remote Sens Symp (IGARSS), 4959–4962Google Scholar
  18. 18.
    Moorthi SM, Misra I, Kaur R, Darji NP, Ramakrishnan R (2011) Kernel based learning approach for satellite image classification using support vector machine. Recent Adv Intell Comput Syst (RAICS), 107–110Google Scholar
  19. 19.
    Nath SS, Mishra G, Kar J, Chakraborty S, Dey N (2014) A survey of image classification methods and techniques. Control, Instrum, Commun Comput Technol (ICCICCT), 554–557Google Scholar
  20. 20.
    Nie L, et al (2017) Enhancing micro-video understanding by harnessing external sounds. ACM Int conf on multimedia, pp 1192–1200Google Scholar
  21. 21.
    Özbay B, Ċimtay Y, Kandaz F (2017) Calculation of vegetation index for short wave infrared hyperspectral images. In: Signal processing and communications applications conference (SIU), pp 1–3Google Scholar
  22. 22.
    Paisitkriangkrai S, Sherrah J, Janney P, van den Hengel A (2016) Semantic labeling of aerial and satellite imagery. IEEE J Selected Topics Appl Earth Observ Remote Sens 9(7):2868–2881CrossRefGoogle Scholar
  23. 23.
    Papadomanolaki M, Vakalopoulou M, Zagoruyko S, Karantzalos K (2016) Benchmarking deep learning frameworks for the classification of very high resolution satellite multispectral data. ISPRS Ann Photogram Remote Sens Spat Inf Sci 3(7):83–88CrossRefGoogle Scholar
  24. 24.
    Sachin Rajan, Sowmya V, Govind D, Soman KP (2017) Dependency of various color and intensity planes on CNN based image classification. In: International symposium on signal processing and intelligent recognition systems, pp 167–177Google Scholar
  25. 25.
    Song S, et al (2017) NeuroStylist: neural compatibility modeling for clothing matching. ACM Int conf on multimedia, pp 753–761Google Scholar
  26. 26.
    Song S, et al (2018) Neural compatibility modeling with attentive knowledge distillation. In: Int ACM SIGIR conference on research & development in information retrieval, pp 5–14Google Scholar
  27. 27.
    Srivatsa S, Sowmya V, Soman K (2016) Least square based fast denoising approach to hyperspectral imagery. Intell Comput Techniques: Theory Practice Appl, 22–24Google Scholar
  28. 28.
    Xu D, Sun L, Luo J, Liu Z (2013) Analysis and denoising of hyperspectral remote sensing image in the curvelet domain. Mathematical Problems in EngineeringGoogle Scholar
  29. 29.
    Zhang C, Hao X, Bai J, Dai M (2014) Improving hyperspectral data classification of satellite imagery by using a sparse based new model with learning dictionary. Hyperspectral Image Signal Process Evol Remote Sens, 1–4Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Computational Engineering and Networking (CEN), Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

Personalised recommendations