Segmentation of Paddy Fields from A Remote Sensing Images Using Ai Based Learning

Authors

  • Rajiv Kumar Assistant Professor, School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, India.
  • Shikha Maheshwari Associate Professor, Directorate of Online Education, Manipal University Jaipur, Rajasthan, India.
  • J. Rajendra Prasad Professor, Department of IT, NRI Institute of Technology, Agiripalli, Andhra Pradesh, India.
  • Rajeeb Lochan Moharana Assistant Professor, Seed Science and Technology, College of Agriculture, OUAT, Bhawanipatna, Odisha, India
  • Chalamalasetty Sarvani Assistant Professor, Department of Computer Science and Applications, Koneru Lakshmaiah Educational Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India.
  • Ramesh Babu P. Associate Professor, Department of Computer Science, College of Engineering and Technology, Wollega University, Nekemte, Oromia Region, Ethiopia.

Keywords:

PCA, Attention model, deep learning, paddy fields

Abstract

Hyperspectral image segmentation (HSI) is a technique that is commonly used to remove redundant and linked data from the original high-dimensional HSI spectral space while at the same time keeping the essential data in a low-dimensional subspace. The use of superpixels has been beneficial to a wide variety of applications, some of which are listed here. In this paper, for the very first time, zeroed in on how well-established super-pixel techniques can serve as a helpful first stage in hyperspectral analysis, with a concentration on classification. In addition to this, we make use of the network that is in the middle of the model, and after that, we employ the technique known as feature fusion to combine the features that originate from the various subnetworks.

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References

Sun, Y., & Zheng, W. (2022). HRNet-and PSPNet-based multiband semantic segmentation of remote sensing images. Neural Computing and Applications, 1-9.

Li, X., Li, Y., Ai, J., Shu, Z., Xia, J., & Xia, Y. (2023). Semantic segmentation of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3+. PloS one, 18(1), e0279097.

Su, Z., Wang, Y., Xu, Q., Gao, R., & Kong, Q. (2022). LodgeNet: Improved rice lodging recognition using semantic segmentation of UAV high-resolution remote sensing images. Computers and Electronics in Agriculture, 196, 106873.

Yang, L., Huang, R., Huang, J., Lin, T., Wang, L., Mijiti, R., ... & Du, X. (2021). Semantic segmentation based on temporal features: Learning of temporal–spatial information from time-series SAR images for paddy rice mapping. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16.

Lan, Y., Huang, K., Yang, C., Lei, L., Ye, J., Zhang, J., ... & Deng, J. (2021). Real-time identification of rice weeds by uav low-altitude remote sensing based on improved semantic segmentation model. Remote Sensing, 13(21), 4370.

Lu, X., Jiao, L., Liu, F., Yang, S., Liu, X., Feng, Z., ... & Chen, P. (2022). Simple and Efficient: A Semisupervised Learning Framework for Remote Sensing Image Semantic Segmentation. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16.

Fan, Z., Zhan, T., Gao, Z., Li, R., Liu, Y., Zhang, L., ... & Xu, S. (2022). Land Cover Classification of Resources Survey Remote Sensing Images Based on Segmentation Model. IEEE Access, 10, 56267-56281.

Li, H. (2022). Multi-Scale Segmentation Method of Remote Sensing Big Data Image Using Deep Learning. Journal of Interconnection Networks, 2242004.

Rajiv, A., Saxena, A.K., Singh, D., Awasthi, A., Dhabliya, D., Yadav, R.K., Gupta, A. IoT and machine learning on smart home-based data and a perspective on fog computing implementation (2023) Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across

Chaudhury, S., Dhabliya, D., Madan, S., Chakrabarti, S. Blockchain technology: A global provider of digital technology and services (2023) Building Secure Business Models Through Blockchain Technology: Tactics, Methods, Limitations, and Performance, pp. 168-193.

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Published

05.12.2023

How to Cite

Kumar, R. ., Maheshwari, S. ., Prasad, J. R. ., Moharana, R. L. ., Sarvani, C. ., & Babu P., R. . (2023). Segmentation of Paddy Fields from A Remote Sensing Images Using Ai Based Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 42–46. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4025

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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