436
Views
5
CrossRef citations to date
0
Altmetric
Research Article

Semi-supervised Classification of Paddy Fields from Dual Polarized Synthetic Aperture Radar (SAR) images using Deep Learning

, , &
Pages 1867-1892 | Received 27 Jul 2020, Accepted 13 Oct 2020, Published online: 20 Dec 2020

References

  • Agarap, A. F. 2018. “Deep Learning Using Rectified Linear Units (Relu).” arXiv Preprint arXiv:1803.08375.
  • Campos-Taberner, M., F. Garca-Haro, G. Camps-Valls, G. Grau-Muedra, F. Nutini, L. Busetto, D. Katsantonis et al. 2017. “Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index.” Remote Sensing 9 (3): 248. doi:10.3390/rs9030248.
  • Changyong, F. E. N. G., W. A. N. G. Hongyue, L. U. Naiji, C. H. E. N. Tian, H. E. Hua, and L. U. Ying. 2014. “Log-transformation and Its Implications for Data Analysis.” Shanghai Archives of Psychiatry 26 (2): 105.
  • Chatterjee, A., J. Saha, J. Mukherjee, S. Aikat, and A. Misra. 2020. “Unsupervised Land Cover Classification of Hybrid and Dual-Polarized Images Using Deep Convolutional Neural Network.” IEEE Geoscience and Remote Sensing Letters 1–5. doi:10.1109/LGRS.2020.2993095.
  • Chung, N. C., B. Miasojedow, M. Startek, and A. Gambin. 2019. “Jaccard/Tanimoto Similarity Test and Estimation Methods for Biological Presence-absence Data.” BMC Bioinformatics 20 (15): 644. doi:10.1186/s12859-019-3118-5.
  • Clauss, K., M. Ottinger, and K. Claudia. 2018. “Mapping Rice Areas with Sentinel-1 Time Series and Superpixel Segmentation.” International Journal of Remote Sensing 39 (5): 1399–1420. doi:10.1080/01431161.2017.1404162.
  • Girshick, R., J. Donahue, T. Darrell, and J. Malik. 2014. “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 580–587.
  • Glorot, X., and Y. Bengio. 2010. “Understanding the Difficulty of Training Deep Feedforward Neural Networks.” In Proceedings of the thirteenth international conference on artificial intelligence and statistics, Sardinia, Italy, 249–256.
  • He, K., G. Gkioxari, P. Dollár, and R. Girshick. 2017. “Mask R-cnn.” In Proceedings of the IEEE international conference on computer vision, Venice, Italy, 2961–2969.
  • Ioffe, S., and C. Szegedy. 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift..” arXiv Preprint arXiv:1502.03167.
  • Jaccard, P. 1912. “The Distribution of the Flora in the Alpine Zone. 1.” New Phytologist 11 (2): 37–50. doi:10.1111/j.1469-8137.1912.tb05611.x.
  • Kingma, D. P., and B. Jimmy. 2014. “Adam: A Method for Stochastic Optimization.” arXiv Preprint arXiv:1412.6980.
  • Mikolov, T., M. Karafiát, L. Burget, J. Černockỳ, and S. Khudanpur. 2010. “Recurrent Neural Network Based Language Model.” In Eleventh annual conference of the international speech communication association, Makuhari, Chiba, Japan.
  • Nasr, G. E., E. A. Badr, and C. Joun. 2002. “Cross Entropy Error Function in Neural Networks.”
  • Nguyen, D. B., and W. Wagner. 2017. “European Rice Cropland Mapping with Sentinel-1 Data: The Mediterranean Region Case Study.” Water 9 (6): 392. doi:10.3390/w9060392.
  • Nwankpa, C., W. Ijomah, A. Gachagan, and S. Marshall. 2018. “Activation Functions: Comparison of Trends in Practice and Research for Deep Learning.” arXiv Preprint arXiv:1811.03378.
  • Onojeghuo, A. O., G. A. Blackburn, Q. Wang, P. M. Atkinson, D. Kindred, and Y. Miao. 2018. “Mapping Paddy Rice Fields by Applying Machine Learning Algorithms to Multi-temporal Sentinel-1A and Landsat Data.” International Journal of Remote Sensing 39 (4): 1042–1067. doi:10.1080/01431161.2017.1395969.
  • QGIS Development Team. 2009. QGIS Geographic Information System. Open Source Geospatial Foundation.
  • Saha, J., and J. Mukhopadhyay. 2019. “RECAL: Reuse of Established CNN Classifer Apropos Unsupervised Learning Paradigm.” arXiv Preprint arXiv:1906 06480.
  • Shi, F. 2015. “Study on a Stratified Sampling Investigation Method for Resident Travel and the Sampling Rate.” Discrete Dynamics in Nature and Society 2015: 1–7. doi:10.1155/2015/496179.
  • Simonyan, K., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large-scale Image Recognition.” arXiv Preprint arXiv:1409.1556.
  • n.d. SNAP - ESA Sentinel Application Platform v2.0.2.
  • Tanimoto, T. T. 1958. “Elementary Mathematical Theory of Classification and Prediction.”
  • Uijlings, J. R. R., K. E. A. Van De Sande, T. Gevers, and A. W. M. Smeulders. 2013. “Selective Search for Object Recognition.” International Journal of Computer Vision 104 (2): 154–171. doi:10.1007/s11263-013-0620-5.
  • Wei, S., H. Zhang, C. Wang, X. Lu, W. Fan, and B. Zhang. 2019b . “Large-scale Rice Mapping of Thailand Using Sentinel-1 Multi-Temporal SAR Data.” In 2019 SAR in Big Data Era (BIGSARDATA), 1–4. Beijing, China: IEEE.
  • Wei, S., H. Zhang, C. Wang, Y. Wang, and X. Lu. 2019a. “Multi-temporal SAR Data Large-scale Crop Mapping Based on U-Net Model.” Remote Sensing 11 (1): 68. doi:10.3390/rs11010068.
  • Zhou, Y., H. Wang, X. Feng, and Y.-Q. Jin. 2016. “Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks.” IEEE Geoscience and Remote Sensing Letters 13 (12): 1935–1939. doi:10.1109/LGRS.2016.2618840.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.