Skip to main content

Advertisement

SpringerLink
Log in

Classification of satellite images for ecology management using deep features obtained from convolutional neural network models

  • Research
  • Published:
Iran Journal of Computer Science Aims and scope Submit manuscript

Abstract

Ecology is the scientific study of balancing biodiversity, which has an impact on natural life and habitats and establishes a strong yet complicated link between the ecosystem’s components. Climate change, wildlife, and other habitats are adversely affected by the presence of anthropogenic pressure. In this respect, it is very important to protect and map natural resources to create efficient ecology management models. In addition, the most efficient method for determining the natural resources and platforms on earth is satellite image analysis. It is effective in monitoring biological diversity, such as ecology management, environmental planning, forestry, agriculture, surface changes, and land use with satellite images. Current classification approaches using satellite imagery often have limited capabilities with feature coding producing mediocre results. Image classification has become quite effective with the development of deep learning models. This study aims to improve the classification performance of deep learning models in satellite image analysis for ecology management using image processing techniques. To manage the classification process more efficiently, convolutional neural network (CNN) models and the neighborhood component analysis (NCA) are used together. Unnecessary features are eliminated with the NCA method. Then, the feature map optimized by the NCA method was used for classification. MobileNetV2, DenseNet201, and ResNet50 were used as feature extractors and six different machine learning classifiers were used as classifiers. As a result, the success rate of classification of satellite images using derived feature vectors has been revealed as 96.46%. According to the experimental results, the use of a combination of feature selection approaches and convolutional neural network models helped to successful classify satellite images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

UC Merced (UCM) Land Use dataset are collected from the USGS National Map Urban Area Imagery collection have been used for this study.

References

  1. Shafaey, M.A., Salem, M.A.M., Ebied, H.M., Al-Berry, M.N., Tolba, M.F.: Deep learning for satellite image classification. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 383–391. Springer, Cham (2018)

  2. Campbell, J.B., Wynne, R.H.: Introduction to remote sensing. Guilford Press, New York (2011)

    Google Scholar 

  3. Costache, R., Bao Pham, Q., Corodescu-Roșca, E., Cîmpianu, C., Hong, H., Thi Thuy Linh, N., et al.: Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential. Remote Sens. 12(9), 1422 (2020)

    Article  Google Scholar 

  4. Zhang, L., Xia, G.S., Wu, T., Lin, L., Tai, X.C.: Deep learning for remote sensing image understanding. J. Sens. 2016, 1–2 (2016)

    Google Scholar 

  5. Marmanis, D., Wegner, J.D., Galliani, S., Schindler, K., Datcu, M., Stilla, U.: Semantic segmentation of aerial images with an ensemble of CNSS. ISPRS Ann. Photogramm. Remote Sens. Spatial Inform. Sci. 2016(3), 473–480 (2016)

    Article  Google Scholar 

  6. Pan, X., Yang, F., Gao, L., Chen, Z., Zhang, B., Fan, H., Ren, J.: Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms. Remote Sens. 11(8), 917 (2019)

    Article  Google Scholar 

  7. Mou, L., Lu, X., Li, X., Zhu, X.X.: Nonlocal graph convolutional networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 58(12), 8246–8257 (2020)

    Article  Google Scholar 

  8. Hajjaji, Y., Boulila, W., Farah, I.R., Romdhani, I., Hussain, A.: Big data and IoT-based applications in smart environments: a systematic review. Comput. Sci. Rev. 39, 100318 (2021)

    Article  Google Scholar 

  9. Fan, Y., Bai, J., Lei, X., Zhang, Y., Zhang, B., Li, K.C., Tan, G.: Privacy preserving based logistic regression on big data. J. Netw. Comput. Appl. 171, 102769 (2020)

    Article  Google Scholar 

  10. Raffini, F., Bertorelle, G., Biello, R., D’Urso, G., Russo, D., Bosso, L.: From nucleotides to satellite imagery: approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12(11), 4508 (2020)

    Article  Google Scholar 

  11. Hoffmann, B.D., Broadhurst, L.M.: The economic cost of managing invasive species in Australia. NeoBiota 31, 1–18 (2016)

    Article  Google Scholar 

  12. Charles, H., Dukes, J.S.: Impacts of invasive species on ecosystem services. In: Biological Invasions, pp. 217–237. Springer, Berlin (2008)

    Google Scholar 

  13. Nan, N., Song, L.: Research on satellite urban transportation and land spatial planning in big data environment. J. Phys. Conf. Ser. 1486(5), 052009 (2020)

    Article  Google Scholar 

  14. Bian, X., Chen, C., Tian, L., Du, Q.: Fusing local and global features for high-resolution scene classification. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 10(6), 2889–2901 (2017)

    Article  Google Scholar 

  15. Yuan, Y., Wan, J., Wang, Q.: Congested scene classification via efficient unsupervised feature learning and density estimation. Pattern Recogn. 56, 159–169 (2016)

    Article  Google Scholar 

  16. Cheng, G., Li, Z., Yao, X., Guo, L., Wei, Z.: Remote sensing image scene classification using bag of convolutional features. IEEE Geosci. Remote Sens. Lett. 14(10), 1735–1739 (2017)

    Article  Google Scholar 

  17. Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., Nemani, R.: Deepsat: a learning framework for satellite imagery. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10 (2015)

  18. Boulemtafes, A., Derhab, A., Challal, Y.: A review of privacy-preserving techniques for deep learning. Neurocomputing 384, 21–45 (2020)

    Article  Google Scholar 

  19. Tu, F., Yin, S., Ouyang, P., Tang, S., Liu, L., Wei, S.: Deep convolutional neural network architecture with reconfigurable computation patterns. IEEE Trans. Very Large Scale Integr. Syst. 25(8), 2220–2233 (2017)

    Article  Google Scholar 

  20. Tanuwidjaja, H.C., Choi, R., Baek, S., Kim, K.: Privacy-preserving deep learning on machine learning as a service—a comprehensive survey. IEEE Access 8, 167425–167447 (2020)

    Article  Google Scholar 

  21. Unnikrishnan, A., Sowmya, V., Soman, K.P.: Deep learning architectures for land cover classification using red and near-infrared satellite images. Multimed. Tools Appl. 78(13), 18379–18394 (2019)

    Article  Google Scholar 

  22. Kadhim, M.A., Abed, M.H.: Convolutional neural network for satellite image classification. In: Asian Conference on Intelligent Information and Database Systems, pp. 165–178. Springer, Cham (2019)

    Google Scholar 

  23. Liu, Q., Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., Nemani, R.: Deepsat v2: feature augmented convolutional neural nets for satellite image classification. Remote Sens. Lett. 11(2), 156–165 (2020)

    Article  Google Scholar 

  24. Pelletier, C., Webb, G.I., Petitjean, F.: Temporal convolutional neural network for the classification of satellite image time series. Remote Sens. 11(5), 523 (2019)

    Article  Google Scholar 

  25. Laban, N., Abdellatif, B., Ebied, H.M., Shedeed, H.A., Tolba, M.F.: Multiscale satellite image classification using deep learning approach. In: Machine Learning and Data Mining in Aerospace Technology, pp. 165–186. Springer, Cham (2020)

    Chapter  Google Scholar 

  26. Xia, M., Tian, N., Zhang, Y., Xu, Y., Zhang, X.: Dilated multi-scale cascade forest for satellite image classification. Int. J. Remote Sens. 41(20), 7779–7800 (2020)

    Article  Google Scholar 

  27. Lunga, D., Gerrand, J., Yang, L., Layton, C., Stewart, R.: Apache spark accelerated deep learning inference for large scale satellite image analytics. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 271–283 (2020)

    Article  Google Scholar 

  28. Nguyen, T.T., Hoang, T.D., Pham, M.T., Vu, T.T., Nguyen, T.H., Huynh, Q.T., Jo, J.: Monitoring agriculture areas with satellite images and deep learning. Appl. Soft Comput. 95, 106565 (2020)

    Article  Google Scholar 

  29. Boulila, W., Sellami, M., Driss, M., Al-Sarem, M., Safaei, M., Ghaleb, F.A.: RS-DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification. Comput. Electron. Agric. 182, 106014 (2021)

    Article  Google Scholar 

  30. Castelluccio, M., Poggi, G., Sansone, C., Verdoliva, L.: Land Use Classification in Remote Sensing Images by Convolutional Neural Networks. arXiv preprint arXiv:1508.00092 (2015)

  31. Scott, G.J., England, M.R., Starms, W.A., Marcum, R.A., Davis, C.H.: Training deep convolutional neural networks for land–cover classification of high-resolution imagery. IEEE Geosci. Remote Sens. Lett. 14(4), 549–553 (2017)

    Article  Google Scholar 

  32. Cheng, G., Han, J., Lu, X.: Remote sensing image scene classification: benchmark and state of the art. Proc. IEEE 105(10), 1865–1883 (2017)

    Article  Google Scholar 

  33. Yang, Y., & Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 270–279 (2010)

  34. Area, U.N.M.U.: UC Merced Land Use Dataset. Available from: http://weegee.vision.ucmerced.edu/datasets/landuse.html (2010)

  35. Chen, C., Zhang, B., Su, H., Li, W., Wang, L.: Land-use scene classification using multi-scale completed local binary patterns. SIViP 10(4), 745–752 (2016)

    Article  Google Scholar 

  36. Mekhalfi, M.L., Melgani, F., Bazi, Y., Alajlan, N.: Land-use classification with compressive sensing multifeature fusion. IEEE Geosci. Remote Sens. Lett. 12(10), 2155–2159 (2015)

    Article  Google Scholar 

  37. Chen, S., Tian, Y.: Pyramid of spatial relatons for scene-level land use classification. IEEE Trans. Geosci. Remote Sens. 53(4), 1947–1957 (2014)

    Article  Google Scholar 

  38. Li, Y., Tao, C., Tan, Y., Shang, K., Tian, J.: Unsupervised multilayer feature learning for satellite image scene classification. IEEE Geosci. Remote Sens. Lett. 13(2), 157–161 (2016)

    Article  Google Scholar 

  39. Luus, F.P., Salmon, B.P., Van den Bergh, F., Maharaj, B.T.J.: Multiview deep learning for land-use classification. IEEE Geosci. Remote Sens. Lett. 12(12), 2448–2452 (2015)

    Article  Google Scholar 

  40. Jiang, Y., Yuan, J., Yu, G.: Randomized spatial partition for scene recognition. In: European Conference on Computer Vision, pp. 730–743. Springer, Berlin (2012)

    Google Scholar 

  41. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated Recognition, Localization and Detection Using Convolutional Networks. arXiv preprint arXiv:1312.6229 (2013)

  42. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  43. Penatti, O.A., Nogueira, K., Dos Santos, J.A.: Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 44–51 (2015)

  44. Özbay, E.: An active deep learning method for diabetic retinopathy detection in segmented fundus images using artificial bee colony algorithm. Artif. Intell. Rev. (2022). https://doi.org/10.1007/s10462-022-10231-3

    Article  Google Scholar 

  45. Özbay, E.: Transformatör-tabanlı evrişimli sinir ağı modeli kullanarak twitter verisinde saldırganlık tespiti. Konya J. Eng. Sci. 10(4), 986–1001 (2022)

    Article  Google Scholar 

  46. Özbay, F.A., Özbay, E.: A new approach for gender detection from voice data: feature selection with optimization methods. J. Fac. Eng. Archit. Gazi Univ. 38(2), 1179–1192 (2023)

    Google Scholar 

Download references

Funding

There is no funding source for this article.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Computer Engineering, Firat University, 23119, Elazig, Turkey

    Erdal Özbay

  2. Faculty of Engineering and Natural Sciences, Computer Engineering, Malatya Turgut Ozal University, 44210, Malatya, Turkey

    Muhammed Yıldırım

Authors
  1. Erdal Özbay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammed Yıldırım
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study’s conception and design. Software coding, methodology, validation, and visualization were performed by MY. Writing, editing, and review of the study, and data curation were performed by EÖ.

Corresponding author

Correspondence to Erdal Özbay.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

This article does not contain any data, or other information from studies or experimentation, with the involvement of human or animal subjects.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Özbay, E., Yıldırım, M. Classification of satellite images for ecology management using deep features obtained from convolutional neural network models. Iran J Comput Sci 6, 185–193 (2023). https://doi.org/10.1007/s42044-022-00133-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42044-022-00133-6

Keywords

Search

Navigation