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Land Use Land Cover Segmentation of LISS-III Multispectral Space-Born Image Using Deep Learning

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Advances in Signal Processing, Embedded Systems and IoT

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 992))

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Abstract

Remote sensing information provides important and sensed data. The study represents semantic segmentation using a fully convolutional network (FCN) for semantic segmentation. Semantic segmentation is a pixel-level classification of images where each pixel is assigned to a respective class. In this present study, four classes—Water Bodies, Vegetation, Uncultivated Land, and Residential areas—were identified. There are various types of machine learning (ML) models as well as deep learning (DL) models to handle segmentation tasks. In this study, deep neural network was used. A fully convolutional network (FCN) with skip connections is trained to take an input image of size 256 * 256 * 3 and outputs a matrix of shape 256 * 256 * 4, i.e., a one-hot encoded version of the mask. The experiment showed that the FCN classifier has a very good capability for land use land cover class detection. The model identifies four classes with 81% of OAA.

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References

  1. Goslee SC (2011) Analyzing remote sensing data in R: the landsat package. J Stat Softw, 43. http://www.jstatsoft.orgl

  2. Cheng G, Zhou P, Han J (2016) Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans Geosci Remote Sens 54(12):7405–7415

    Article  Google Scholar 

  3. Gmez-Chova L, Tuia D, Moser G, Camps-Valls G (2015) Multimodal classification of remote sensing images: a review and future directions. IEEE Proc 103(9):1560–1584

    Article  Google Scholar 

  4. Zhang L, Zhang L, Du B (2016) Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosci Remote Sens Mag 4(2):22–40

    Article  Google Scholar 

  5. Jaiswal RK, Saxena R, Mukherjee S (1999) Application of remote sensing technology for land use/land cover change analysis. J Indian Soc Remote Sens 27(2):123–128

    Article  Google Scholar 

  6. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  7. Wen-bo F, Tao S, Ji L, Bao-wei Y, Fu-Xin F (2018) Review of principle and application of deep learning. Comput Sci 45(S1):11–15+40

    Google Scholar 

  8. Richards JA, Richards JA (1999) Remote sensing digital image analysis. vol 3, pp 10–38. Springer, Berlin

    Google Scholar 

  9. Mishra C, Gupta DL (2017) Deep machine learning and neural networks: an overview. IAES Int J Artif Intell 6(2):66

    Google Scholar 

  10. Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Observations Remote Sens 7:2094–2107. https://doi.org/10.1109/JSTARS.2014.2329330

    Article  Google Scholar 

  11. Yin X, Chen W, Wu X, Yue H (2017) Fine-tuning and visualization of convolutional neural networks. pp 1310–1315. https://doi.org/10.1109/ICIEA.2017.8283041

  12. Liu P, Zhang H, Eom K (2016) Active deep learning for classification of hyperspectral images. IEEE J Sel Top Appl Earth Observations Remote Sens, pp 1–13. https://doi.org/10.1109/JSTARS.2016.2598859

  13. Piramanayagam S, Schwartzkopf W, Koehler FW, Saber E (2016) Classification of remote sensed images using random forests and deep learning framework. In: Image and signal processing for remote sensing XXII, vol 10004, pp 205–212. SPIE

    Google Scholar 

  14. Yu X, Wu X, Luo C, Ren P (2017) Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. GISci Remote Sens 54:1–18. https://doi.org/10.1080/15481603.2017.1323377

    Article  Google Scholar 

  15. Yang Z, Mu X-D, Zhao F-A (2018) Scene classification of remote sensing image based on deep network and multi-scale features fusion. Optik, 171. https://doi.org/10.1016/j.ijleo.2018.06.024

  16. Guo D, Xia Y, Luo X (2020) Scene classification of remote sensing images based on saliency dual attention residual network. IEEE Access 8:6344–6357. https://doi.org/10.1109/ACCESS.2019.2963769

    Article  Google Scholar 

  17. Xu X, Chen Y, Zhang J, Chen Y, Anandhan P, Manickam A (2020) A novel approach for scene classification from remote sensing images using deep learning methods. Eur J Remote Sen, 54:1–13. https://doi.org/10.1080/22797254.2020.1790995

  18. Alhichri H, Alsuwayed A, Bazi Y, Ammour N, Alajlan N (2021) Classification of remote sensing images using efficientNet-B3 CNN model with attention. IEEE Access. pp 1–1. https://doi.org/10.1109/ACCESS.2021.3051085

  19. Pires de Lima R, Marfurt K (2019) Convolutional neural network for remote-sensing scene classification: transfer learning analysis. Remote Sens, 12:86. https://doi.org/10.3390/rs12010086

  20. Mohanty SP, Czakon J, Kaczmarek KA, Pyskir A, Tarasiewicz P, Kunwar S, Rohrbach J, Luo D, Prasad M, Fleer S, Göpfert JP (2020). Deep learning for understanding satellite imagery: an experimental survey. Front Artif Intell, 3:534696

    Google Scholar 

  21. Lateef F, Ruichek Y (2019) Survey on semantic segmentation using deep learning techniques. Neurocomputing 338:321–348. https://doi.org/10.1016/j.neucom.2019.02.003

    Article  Google Scholar 

  22. Hao S, Zhou Y, Guo Y (2020) A brief survey on semantic segmentation with deep learning. Neurocomputing 406:302–321. https://doi.org/10.1016/j.neucom.2019.11.118

    Article  Google Scholar 

  23. Thoma M (2016) A survey of semantic segmentation. CoRR. abs/1602.06541

    Google Scholar 

  24. Atif N, Bhuyan M, Ahamed S (2019) A review on semantic segmentation from a modern perspective. In: 2019 international conference on electrical, electronics and computer engineering (UPCON), 8–10 Nov 2019, Aligarh, India. IEEE, Piscataway, NJ, pp 1–6

    Google Scholar 

  25. Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D (2020) Image segmentation using deep learning: a survey. arXiv preprint arXiv: 2001.05566

    Google Scholar 

  26. Fan J, Cao X, Yap P-T (2018) Birnet: brain image registration using dual-supervised fully convolutional networks. Med Image Anal 54:02

    Google Scholar 

  27. Shelhamer E, Long J, Darrell T (2016) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 39:1–1

    Google Scholar 

  28. Bi L, Kim J, Ahn E, Kumar A, Feng DDF, Fulham M (2019) Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recogn 85:78–89

    Google Scholar 

  29. Huang Y, Zhou F, Gilles J (2019) Empirical curvelet based fully convolutional network for supervised texture image segmentation. Neurocomputing 349:04

    Article  Google Scholar 

  30. Li C, Wang X, Liu W, Latecki LJ, Wang B, Huang J (2019) Weakly supervised mitosis detection in breast histopathology images using concentric loss. Med Image Anal 53, p 02

    Google Scholar 

  31. Such F, Pillai S, Brockler F, Singh V, Hutkowski P, Ptucha R (2018) Intelligent character recognition using fully convolutional neural networks. Pattern Recogn 88:12

    Google Scholar 

  32. Kumar S, Kumar N, Rishabh IK, Keshari V (2021) Automated brain tumour detection using deep learning via convolution neural networks (CNN). Int J Cur Res Rev 13(02):148

    Article  Google Scholar 

  33. Guo Y, Liao J, Shen G (2021) Mapping large-scale mangroves along the maritime silk road from 1990 to 2015 using a novel deep learning model and landsat data. Remote Sens 13:245. https://doi.org/10.3390/rs13020245

    Article  Google Scholar 

  34. Guo M, Yu Z, Xu Y, Huang Y, Li C (2021) ME-Net: a deep convolutional neural network for extracting mangrove using sentinel-2A data. Remote Sens 13:1292. https://doi.org/10.3390/rs13071292

    Article  Google Scholar 

  35. Wan L, Zhang H, Lin G, Lin H (2019) A small-patched convolutional neural network for mangrove mapping at species level using high-resolution remote-sensing image. Ann GIS 25:45–55. https://doi.org/10.1080/19475683.2018.1564791

    Article  Google Scholar 

  36. Hoeser T, Bachofer F, Kuenzer C (2020) Object detection and image segmentation with deep learning on earth observation data: a review—part II: applications. Remote Sens 12:3053. https://doi.org/10.3390/rs12183053

    Article  Google Scholar 

  37. Hosseiny B, Mahdianpari M, Brisco B, Mohammadimanesh F, Salehi B (2021) WetNet: a spatial-temporal ensemble deep learning model for wetland classification using sentinel-1 and sentinel-2. IEEE Trans Geosci Remote Sens, pp 1–14

    Google Scholar 

  38. Castelluccio M, Poggi G, Sansone C, Verdoliva L (2015) Land use classification in remote sensing images by convolutional neural networks. Aug 2015. arXiv:1508.00092

  39. 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. IEEE, San Juan, Puerto Rico, pp 770–778

    Google Scholar 

  40. Li J, Lin D, Wang Y, Xu G, Zhang Y, Ding C, Zhou Y (2020) Deep discriminative representation learning with attention map for scene classification. Remote Sens 12:1366. https://doi.org/10.3390/rs12091366

    Article  Google Scholar 

  41. Sonune N (2020) Land cover classification with EuroSAT dataset. https://www.kaggle.com/nilesh789/land-cover-classification-with-eurosat-dataset

  42. Remote Sensing Notes edited by Japan Association of Remote Sensing © JARS 1999

    Google Scholar 

  43. Xu J, Song L, Zhong D, Zhao Z, Zhao K (2013) Remote sensing image classification based on a modified self-organizing neural network with a priori knowledge. Sens Transducers 153:29–36

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Applications, Atmiya University, Rajkot, India

    Nirav Desai & Parag Shukla

Authors
  1. Nirav Desai
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  2. Parag Shukla
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Corresponding author

Correspondence to Nirav Desai .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Raghu Institute of Technology, Visakhapatnam, India

    V.V.S.S.S. Chakravarthy

  2. Department of Electronics Engineering, Faculty of Engineering and Technology, Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  3. Autonomous University of Baja California, Mexicali, Baja California, Mexico

    Wendy Flores Fuentes

  4. Department of Electronics and Telecommunication, Universitat Ramon Llull, Barcelona, Spain

    Jaume Anguera

  5. Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhra Pradesh, India

    K. Padma Vasavi

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Desai, N., Shukla, P. (2023). Land Use Land Cover Segmentation of LISS-III Multispectral Space-Born Image Using Deep Learning. In: Chakravarthy, V., Bhateja, V., Flores Fuentes, W., Anguera, J., Vasavi, K.P. (eds) Advances in Signal Processing, Embedded Systems and IoT . Lecture Notes in Electrical Engineering, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-19-8865-3_42

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  • DOI: https://doi.org/10.1007/978-981-19-8865-3_42

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8864-6

  • Online ISBN: 978-981-19-8865-3

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