73
Views
0
CrossRef citations to date
0
Altmetric
Research Article

PolSAR image classification based on TCN deep learning: a case study of greater Cairo

, , , &
Pages 214-231 | Received 22 Jan 2023, Accepted 17 Dec 2023, Published online: 01 Jan 2024

References

  • Aljabri, A.A., et al., 2023. Extracting feature fusion and co-saliency clusters using transfer learning techniques for improving remote sensing scene classification. Optik, 273, 170408. doi:10.1016/j.ijleo.2022.170408
  • Bai, S., Kolter, J.Z., and Koltun, V., 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271
  • Cameron, W.L. and Leung, L.K., 1990. Feature motivated polarization scattering matrix decomposition. In: Paper presented at the IEEE International Conference on Radar, 07-10 May 1990 Arlington, VA, USA.
  • Chen, L., et al. 2010. Feature evaluation and selection for polarimetric SAR image classification. In: Paper presented at the IEEE 10th International Conference On Signal Processing Proceedings, 24-28 October 2010 Beijing, China.
  • Chen, S.-W. and Tao, C.-S., 2018. PolSAR image classification using polarimetric-feature-driven deep convolutional neural network. IEEE Geoscience and Remote Sensing Letters, 15 (4), 627–631. doi:10.1109/LGRS.2018.2799877
  • Cheng, G., et al., 2020. Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3735–3756. doi:10.1109/JSTARS.2020.3005403
  • Cheng, J., et al., 2022. PolSAR image classification with multiscale superpixel-based graph convolutional network. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14. doi:10.1109/TGRS.2021.3079438
  • Cho, K., et al., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078
  • Dong, H., Zhang, L., and Zou, B., 2020. PolSAR image classification with lightweight 3D convolutional networks. Remote Sensing, 12 (3), 396. doi:10.3390/rs12030396
  • Freeman, A. and Durden, S.L., 1998. A three-component scattering model for polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 36 (3), 963–973. doi:10.1109/36.673687
  • Gao, F., et al., 2021. A softmax classifier for high-precision classification of ultrasonic similar signals. Ultrasonics, 112, 106344. doi:10.1016/j.ultras.2020.106344
  • Gigli, G., Sabry, R., and Lampropoulos, G. 2007. Classifier combination and feature selection methods for polarimetric SAR classification. Paper presented at the Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications.
  • Goodfellow, I., et al., 2020. Generative adversarial networks. Communications of the ACM, 63 (11), 139–144. doi:10.1145/3422622
  • Hagag, A., et al., 2021. Dual link distributed source coding scheme for the transmission of satellite hyperspectral imagery. Journal of Visual Communication and Image Representation, 78, 103117. doi:10.1016/j.jvcir.2021.103117
  • Hajnsek, I., Pottier, E., and Cloude, S.R., 2003. Inversion of surface parameters from polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 41 (4), 727–744. doi:10.1109/TGRS.2003.810702
  • Hassanien, A.E., Al-Qaheri, H., and El-Dahshan, E.-S.A., 2011. Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network. Applied Soft Computing, 11 (2), 2035–2041. doi:10.1016/j.asoc.2010.07.001
  • Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural Computation, 9 (8), 1735–1780. doi:10.1162/neco.1997.9.8.1735
  • Huang, H. and Xu, K., 2019. Combing triple-part features of convolutional neural networks for scene classification in remote sensing. Remote Sensing, 11 (14), 1687. doi:10.3390/rs11141687
  • Jamali, A., et al., 2023. Local window attention transformer for polarimetric SAR image classification. IEEE Geoscience and Remote Sensing Letters, 20, 1–5. doi:10.1109/LGRS.2023.3239263
  • Ji, K. and Wu, Y., 2015. Scattering mechanism extraction by a modified cloude-pottier decomposition for dual polarization SAR. Remote Sensing, 7 (6), 7447–7470. doi:10.3390/rs70607447
  • Jing, H., et al., 2021. PSRN: Polarimetric Space Reconstruction Network for PolSAR image semantic segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10716–10732. doi:10.1109/JSTARS.2021.3116062
  • Kong, J., et al., 1988. Identification of terrain cover using the optimum polarimetric classifier. Journal of Electromagnetic Waves and Applications, 2 (2), 171–194.
  • Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60 (6), 84–90. doi:10.1145/3065386
  • Krogager, E., 1990. New decomposition of the radar target scattering matrix. Electronics Letters, 18 (26), 1525–1527. doi:10.1049/el:19900979
  • Lee, J.-S., Grunes, M.R., and Kwok, R., 1994. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution. International Journal of Remote Sensing, 15 (11), 2299–2311. doi:10.1080/01431169408954244
  • Lee, J.-S. and Pottier, E., 2017. Polarimetric radar imaging: from basics to applications. Boca Raton: CRC press.
  • Li-Wen, Z., Xiao-Guang, Z., and Yong-Mei, J. 2007. Iterative classification of polarimetric SAR image based on the freeman decomposition and scattering entropy. In: Paper presented at the 2007 1st Asian and Pacific Conference on Synthetic Aperture Radar, 05-09 November 2007 Huangshan, China.
  • Liu, B.-D., et al., 2019. Weighted spatial pyramid matching collaborative representation for remote-sensing-image scene classification. Remote Sensing, 11 (5), 518. doi:10.3390/rs11050518
  • Liu, G., et al., 2021. Multiobjective evolutionary algorithm assisted stacked autoencoder for PolSAR image classification. Swarm and Evolutionary Computation, 60, 100794. doi:10.1016/j.swevo.2020.100794
  • Liu, S.J., Luo, H., and Shi, Q., 2021. Active ensemble deep learning for polarimetric synthetic aperture radar image classification. IEEE Geoscience and Remote Sensing Letters, 18 (9), 1580–1584. doi:10.1109/LGRS.2020.3005076.
  • Liu, X., et al., 2018. Polarimetric convolutional network for PolSAR image classification. IEEE Transactions on Geoscience and Remote Sensing, 57 (5), 3040–3054. doi:10.1109/TGRS.2018.2879984
  • Long, J., Shelhamer, E., and Darrell, T. 2015. Fully convolutional networks for semantic segmentation. In: Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, 07-12 June 2015 Boston, MA, USA.
  • Lu, X., Zheng, X., and Yuan, Y., 2017. Remote sensing scene classification by unsupervised representation learning. IEEE Transactions on Geoscience and Remote Sensing, 55 (9), 5148–5157. doi:10.1109/TGRS.2017.2702596
  • Lv, Y., et al., 2019. An end-to-end local-global-fusion feature extraction network for remote sensing image scene classification. Remote Sensing, 11 (24), 3006. doi:10.3390/rs11243006
  • Mohammad, R., et al., 2022. Investigating the radar scattering response of different land-features in the greater Cairo area of Egypt, using the full-polarimetric SAR data. Egyptian Journal of Pure and Applied Science, 59 (2), 75–89. doi:10.21608/ejaps.2022.110358.1016
  • Nair, V. and Hinton, G.E. 2010. Rectified linear units improve restricted boltzmann machines. Paper presented at the Icml.
  • Nie, W., et al., 2022. A deep reinforcement learning-based framework for PolSAR imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–15. doi:10.1109/TGRS.2021.3093474
  • Nogueira, K., Penatti, O.A., and Dos Santos, J.A., 2017. Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, 539–556. doi:10.1016/j.patcog.2016.07.001
  • Oord, A.V.D., et al., 2016. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499
  • Panchal, S. and Thakker, R.A., 2017a. Contourlet transform with sparse representation-based integrated approach for image pansharpening. IETE Journal of Research, 63 (6), 823–833. doi:10.1080/03772063.2017.1326294
  • Panchal, S. and Thakker, R.A., 2017b. Improved image pansharpening technique using nonsubsampled contourlet transform with sparse representation. The Journal of the Indian Society of Remote Sensing, 45 (3), 385–394. doi:10.1007/s12524-016-0608-z
  • Pearlmutter, B.A., 1989. Learning state space trajectories in recurrent neural networks. Neural Computation, 1 (2), 263–269. doi:10.1162/neco.1989.1.2.263
  • Pottier, E. 1993. Dr. JR Huynen’s main contributions in the development of polarimetric radar techniques and how the’Radar targets phenomenological Concept’becomes a theory. Paper presented at the Radar polarimetry.
  • Qi, X., Wang, T., and Liu, J. 2017. Comparison of support vector machine and softmax classifiers in computer vision. In: Paper presented at the 2017 Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 08-10 December 2017 Harbin, China.
  • Ren, B., et al., 2021. A mutual information-based self-supervised learning Model for PolSAR land cover classification. IEEE Transactions on Geoscience and Remote Sensing, 59 (11), 9224–9237. doi:10.1109/TGRS.2020.3048967.
  • Shang, R., et al., 2020. Dense connection and depthwise separable convolution based CNN for polarimetric SAR image classification. Knowledge-Based Systems, 194, 105542. doi:10.1016/j.knosys.2020.105542
  • Shawky, O.A., et al., 2020. Remote sensing image scene classification using CNN-MLP with data augmentation. Optik, 221, 165356. doi:10.1016/j.ijleo.2020.165356
  • Shimoni, M., et al., 2009. Fusion of PolSAR and PolInSAR data for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11 (3), 169–180. doi:10.1016/j.jag.2009.01.004
  • Uhlmann, S. and Kiranyaz, S., 2013. Integrating color features in polarimetric SAR image classification. IEEE Transactions on Geoscience and Remote Sensing, 52 (4), 2197–2216. doi:10.1109/TGRS.2013.2258675
  • van Zyl, J.J., Arii, M., and Kim, Y., 2011. Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues. IEEE Transactions on Geoscience and Remote Sensing, 49 (9), 3452–3459. doi:10.1109/TGRS.2011.2128325
  • Wang, Z., et al., 2019. Domain adaptation with discriminative distribution and manifold embedding for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 16 (7), 1155–1159. doi:10.1109/LGRS.2018.2889967
  • Xie, W., et al., 2017. POLSAR image classification via wishart-AE model or wishart-CAE model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (8), 3604–3615. doi:10.1109/JSTARS.2017.2698076
  • Xie, W., et al., 2018. POLSAR image classification via clustering-WAE classification model. Institute of Electrical and Electronics Engineers Access, 6, 40041–40049. doi:10.1109/ACCESS.2018.2852768
  • Xie, W., et al., 2020. PolSAR image classification via a novel semi-supervised recurrent complex-valued convolution neural network. Neurocomputing, 388, 255–268. doi:10.1016/j.neucom.2020.01.020
  • Xingjian, S., et al., 2015. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Paper presented at the Advances in neural information processing systems.
  • Xu, Y., et al., 2018. Spectral–spatial unified networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 56 (10), 5893–5909. doi:10.1109/TGRS.2018.2827407
  • Yamaguchi, Y., et al., 2005. Four-component scattering model for polarimetric SAR image decomposition. IEEE Transactions on Geoscience and Remote Sensing, 43 (8), 1699–1706. doi:10.1109/TGRS.2005.852084
  • Yang, C., et al., 2019. CNN-based polarimetric decomposition feature selection for PolSAR image classification. IEEE Transactions on Geoscience and Remote Sensing, 57 (11), 8796–8812. doi:10.1109/TGRS.2019.2922978
  • Yang, C., et al., 2022. N-Cluster loss and hard sample generative deep metric learning for PolSAR image classification. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–16. doi:10.1109/TGRS.2021.3099840
  • Yu, S., Jia, S., and Xu, C., 2017. Convolutional neural networks for hyperspectral image classification. Neurocomputing, 219, 88–98. doi:10.1016/j.neucom.2016.09.010
  • Zhang, W., Tang, P., and Zhao, L., 2019. Remote sensing image scene classification using CNN-CapsNet. Remote Sensing, 11 (5), 494. doi:10.3390/rs11050494
  • Zhang, Z., et al., 2017. Complex-valued convolutional neural network and its application in polarimetric SAR image classification. IEEE Transactions on Geoscience and Remote Sensing, 55 (12), 7177–7188. doi:10.1109/TGRS.2017.2743222
  • Zhou, Y., et al., 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
  • Zhu, X.X., et al., 2021. Deep learning meets SAR: concepts, models, pitfalls, and perspectives. IEEE Geoscience and Remote Sensing Magazine, 9 (4), 143–172. doi:10.1109/MGRS.2020.3046356
  • Zou, T., et al., 2009. Polarimetric SAR image classification using multifeatures combination and extremely randomized clustering forests. EURASIP Journal on Advances in Signal Processing, 2010 (1), 1–9. doi:10.1155/2010/465612

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.