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Enhanced FCN for farmland extraction from remote sensing image

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Abstract

As farmland being the foundation of national agribusiness, it is of paramount significance to obtain data more efficiently about the distribution of farmland for further agricultural resource monitoring. Through classification of Remote Sensing (RS) images combined with deep learning approaches, however, previous studies did not attach enough attention to boundary ambiguity, thus achieving relatively low accuracy and demands artificial refinements in farmland extraction. To remedy flaws in current approaches and improve overall accuracy, our work reviewed relevant literature and utilized K-Means model, U-Net model and DeelLabV3 model respectively, to refine and make adjustments to farmland extraction model of RS image afterwards. After model training and parameter tuning, the final result of the classification model reached 95.76% in terms of overall accuracy, and the average cross-comparison ratio in farmland recognition rate reached 85.44%. We closed our paper with future directions and possible improvements to our work.

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References

  1. Agarap AF (2018) Deep learning using rectified linear units (relu). arXiv:1803.08375

  2. Alshehhi R, Marpu PR, Woon WL, Dalla Mura M (2017) Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens 130:139–149

    Article  Google Scholar 

  3. Appeaning Addo K (2010) Urban and peri-urban agriculture in developing countries studied using remote sensing and in situ methods. Remote Sens 2(2):497–513

    Article  Google Scholar 

  4. Atzberger C (2013) Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sens 5(2):949–981

    Article  Google Scholar 

  5. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12):2481–2495

    Article  Google Scholar 

  6. Barbedo JGA, et al. (2018) A review on the automatic segmentation and classification of agricultural areas in remotely sensed images. Embrapa Document Series 156:1–32

    Google Scholar 

  7. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  8. Bulat A, Tzimiropoulos G (2016) Human pose estimation via convolutional part heatmap regression. In: European conference on computer vision. Springer, pp 717–732

  9. Buslaev A, Seferbekov S, Iglovikov V, Shvets A (2018) Fully convolutional network for automatic road extraction from satellite imagery. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 207–210

  10. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40(4):834–848

    Article  Google Scholar 

  11. Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C (2016) The importance of skip connections in biomedical image segmentation. In: Deep learning and data labeling for medical applications. Springer, pp 179–187

  12. Dumoulin V, Visin F (2016) A guide to convolution arithmetic for deep learning. arXiv:1603.07285

  13. Fan E (2000) Extended tanh-function method and its applications to nonlinear equations. Phys Lett A 277(4-5):212–218

    Article  MathSciNet  Google Scholar 

  14. Foody GM, Mathur A (2004) A relative evaluation of multiclass image classification by support vector machines. IEEE Trans Geosci Remote Sens 42(6):1335–1343

    Article  Google Scholar 

  15. Gualtieri JA, Cromp RF (1999) Support vector machines for hyperspectral remote sensing classification. In: 27th AIPR workshop: advances in computer-assisted recognition. International Society for Optics and Photonics, vol 3584, pp 221–232

  16. Han J, Moraga C (1995) The influence of the sigmoid function parameters on the speed of backpropagation learning. In: International workshop on artificial neural networks. Springer, pp 195–201

  17. Hariharan B, Arbeláez P, Girshick R, Malik J (2015) Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 447–456

  18. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  19. Honari S, Yosinski J, Vincent P, Pal C (2016) Recombinator networks: Learning coarse-to-fine feature aggregation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5743–5752

  20. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  21. Huang C, Davis L, Townshend J (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23(4):725–749

    Article  Google Scholar 

  22. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  23. Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of physiology 160 (1):106–154

    Article  Google Scholar 

  24. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25:1097–1105

    Google Scholar 

  25. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551

    Article  Google Scholar 

  26. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  27. Liang S, Wang J (2019) Advanced remote sensing: terrestrial information extraction and applications. Academic Press

  28. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125

  29. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  30. Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2016) Fully convolutional neural networks for remote sensing image classification. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, pp 5071–5074

  31. Masci J, Meier U, Cireşan D, Schmidhuber J (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. In: International conference on artificial neural networks. Springer, pp 52–59

  32. Nagi J, Ducatelle F, Di Caro GA, Cireşan D, Meier U, Giusti A, Nagi F, Schmidhuber J, Gambardella LM (2011) Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: 2011 IEEE International conference on signal and image processing applications (ICSIPA). IEEE, 342-347

  33. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, pp 234–241

  34. Shi W, Caballero J, Theis L, Huszar F, Aitken A, Ledig C, Wang Z (2016) Is the deconvolution layer the same as a convolutional layer? arXiv:1609.07009

  35. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  36. Sun K, Zhao Y, Jiang B, Cheng T, Xiao B, Liu D, Mu Y, Wang X, Liu W, Wang J (2019) High-resolution representations for labeling pixels and regions. arXiv:1904.04514

  37. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31

  38. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  39. Thenkabail PS (2010) Global croplands and their importance for water and food security in the twenty-first century: towards an ever green revolution that combines a second green revolution with a blue revolution

  40. Thenkabail PS, Biradar CM, Noojipady P, Dheeravath V, Li Y, Velpuri M, Gumma M, Gangalakunta ORP, Turral H, Cai X et al (2009) Global irrigated area map (giam), derived from remote sensing, for the end of the last millennium. Int J Remote Sens 30(14):3679–3733

    Article  Google Scholar 

  41. Thenkabail PS, Knox JW, Ozdogan M, Gumma MK, Congalton RG, Wu Z, Milesi C, Finkral A, Marshall M, Mariotto I et al (2012) Assessing future risks to agricultural productivity, water resources and food security: how can remote sensing help?, PE&RS. Photogramm Eng Remote Sens 78(8):773–782

    Google Scholar 

  42. Thenkabail P, Lyon JG, Turral H, Biradar C (2009) Remote sensing of global croplands for food security CRC Press

  43. Thenkabail PS, Wu Z (2012) An, automated cropland classification algorithm (Acca) for Tajikistan by combining landsat, modis, and secondary data. Remote Sens 4(10):2890–2918

    Article  Google Scholar 

  44. Thyagharajan K, Vignesh T (2019) Soft computing techniques for land use and land cover monitoring with multispectral remote sensing images: a review. Archives of Computational Methods in Engineering 26(2):275–301

    Article  Google Scholar 

  45. Valle R, Buenaposada JM, Valdes A, Baumela L (2018) A deeply-initialized coarse-to-fine ensemble of regression trees for face alignment. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 585–601

  46. Vignesh T, Thyagharajan K, Jeyavathana RB, Kanimozhi K (2021) Land use and land cover classification using recurrent neural networks with shared layered architecture. In: 2021 International Conference on Computer Communication and Informatics (ICCCI). IEEE, pp 1–6

  47. Vignesh T, Thyagharajan K (2017) Water bodies identification from multispectral images using gabor filter, and canny edge detection methods. In: 2017 International conference on information communication and embedded systems (ICICES). IEEE, pp 1–5

  48. Vigneshl T, Thyagharajan K (2014) Local binary pattern texture feature for satellite imagery classification. In: 2014 International conference on science engineering and management research (ICSEMR). IEEE, pp 1–6

  49. Wang Z, Bovik AC (2009) Mean squared error: love it or leave it? a new look at signal fidelity measures. IEEE Signal Proc Mag 26(1):98–117

    Article  Google Scholar 

  50. Xu L, Ren JS, Liu C, Jia J (2014) Deep convolutional neural network for image deconvolution. Advances in Neural Information Processing Systems 27:1790–1798

    Google Scholar 

  51. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer, pp 818–833

  52. Zeiler MD, Taylor GW, Fergus R (2011) Adaptive deconvolutional networks for mid and high level feature learning. In: 2011 International conference on computer vision. IEEE, pp 2018–2025

  53. Zhang Z, Sabuncu MR (2018) Generalized cross entropy loss for training deep neural networks with noisy labels. In: 32Nd Conference on neural information processing systems (neurIPS)

  54. Zhang L, Zhang L, Du B (2016) Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine 4(2):22–40

    Article  Google Scholar 

  55. Zhao W, Du S, Emery WJ (2017) Object-based convolutional neural network for high-resolution imagery classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(7):3386–3396

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61802233) and Natural Science Foundation of Shandong Province (No.ZR2019LZH013 and ZR2020LZH010). Thanks to Qilu University of Technology (Shandong Academy of Sciences) Science, Education and Industry Integration Innovation Pilot Project “Supercomputer Internet Key Technology Research and Application Demonstration”. Zhiqiang Wei and Yuhan Zhao are the corresponding authors.

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Authors and Affiliations

  1. College of Information Science and Engineering, Ocean University of China, Qingdao, China

    Jingshan Pan & Zhiqiang Wei

  2. Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Jingshan Pan, Yuhan Zhao, Yan Zhou, Xunyu Lin & Wei Zhang

  3. School of Computer Science, China University of Geosciences, Wuhan, China

    Chang Tang

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  1. Jingshan Pan
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Correspondence to Zhiqiang Wei or Yuhan Zhao.

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Pan, J., Wei, Z., Zhao, Y. et al. Enhanced FCN for farmland extraction from remote sensing image. Multimed Tools Appl 81, 38123–38150 (2022). https://doi.org/10.1007/s11042-022-12141-6

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  • DOI: https://doi.org/10.1007/s11042-022-12141-6

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