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Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 49, 2023 - Issue 1
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Research Article

A Novel Classification Method for PolSAR Image Combining the Deep Learning Model and Adaptive Boosting of Shallow Classifiers

Une nouvelle méthode de classification des images PolSAR combinant le modèle d’apprentissage profond et l’optimisation adaptative des algorithmes traditionnels

ORCID Icon, , &
Article: 2257331 | Received 13 Apr 2023, Accepted 11 Aug 2023, Published online: 15 Sep 2023

References

  • Aghababaee, H., Amini, J., and Tzeng, Y.C. 2013. “Contextual PolSAR image classification using fractal dimension and support vector machines.” European Journal of Remote Sensing, Vol. 46(No. 1): pp. 317–332. doi:10.5721/EuJRS20134618.
  • Bi, H.X., Xu, F., Wei, Z.Q., Xue, Y., and Xu, Z.B. 2019. “An active deep learning approach for minimally supervised PolSAR image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(No. 11): pp. 9378–9395. doi:10.1109/TGRS.2019.2926434.
  • Breiman, L. 2001. “Random forests.” Machine Learning, Vol. 45(No. 1): pp. 5–32. doi:10.1023/A:1010933404324.
  • Chang, C.C., and Lin, C.J. 2011. “Libsvm: A library for support vector machines.” ACM Transactions on Intelligent Systems and Technology, Vol. 2(No. 3): pp. 1–27. doi:10.1145/1961189.1961199.
  • Cheng, J.D., Zhang, F., Xiang, D.L., Yin, Q., Zhou, Y.S., and Wang, W. 2021. “PolSAR image land cover classification based on hierarchical capsule network.” Remote Sensing, Vol. 13(No. 16): pp. 3132. doi:10.3390/rs13163132.
  • Cheng, X.G., Huang, W.L., and Gong, J.Y. 2013. “A decomposition-free scattering mechanism classification method for PolSAR images with Neumann’s model.” Remote Sensing Letters, Vol. 4(No. 12): pp. 1176–1184. doi:10.1080/2150704X.2013.858840.
  • Cloude, S.R., and Pottier, E. 1997. “An entropy based classification scheme for land applications of polarimetric SAR.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 35(No. 1): pp. 68–78. doi:10.1109/36.551935.
  • Deng, L., Yan, Y-N., and Sun, C. 2015. “Use of sub-aperture decomposition for supervised PolSAR classification in urban area.” Remote Sensing, Vol. 7(No. 2): pp. 1380–1396. doi:10.3390/rs70201380.
  • Doğan, H., and Akay, O. 2010. “Using AdaBoost classifiers in a hierarchical framework for classifying surface images of marble slabs.” Expert Systems with Applications, Vol. 37(No. 12): pp. 8814–8821. doi:10.1016/j.eswa.2010.06.019.
  • Dong, H.W., Zou, B., Zhang, L.M., and Zhang, S.Y. 2020. “Automatic design of CNNS via differentiable neural architecture search for PolSAR image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 58(No. 9): pp. 6362–6375. doi:10.1109/TGRS.2020.2976694.
  • Dou, P., Chen, Y.B., and Yue, H.Y. 2018. “Remote-sensing imagery classification using multiple classification algorithm-based AdaBoost.” International Journal of Remote Sensing, Vol. 39(No. 3): pp. 619–639. doi:10.1080/01431161.2017.1390276.
  • Duan, Y., Chen, N., and Chen, Y.B. 2019. “A novel PolSAR image classification method based on optimal polarimetric features and contextual information.” Canadian Journal of Remote Sensing, Vol. 45(No. 6): pp. 795–813. doi:10.1080/07038992.2019.1697222.
  • Entezari, I., Motagh, M., and Mansouri, B. 2012. “Comparison of the performance of l-band polarimetric parameters for land cover classification.” Canadian Journal of Remote Sensing, Vol. 38(No. 5): pp. 629–643. doi:10.5589/m12-051.
  • Gadhiya, T., and Roy, A.K. 2020. “Superpixel-driven optimized Wishart network for fast PolSAR image classification using global k-means algorithm.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 58(No. 1): pp. 97–109. doi:10.1109/TGRS.2019.2933483.
  • Ghimire, B., Rogan, J., Galiano, V.R., Panday, P., and Neeti, N. 2012. “An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA.” GIScience & Remote Sensing, Vol. 49(No. 5): pp. 623–643. doi:10.2747/1548-1603.49.5.623.
  • He, C., Tu, M.X., Xiong, D.H., and Liao, M.S. 2020. “Nonlinear manifold learning integrated with fully convolutional networks for PolSAR image classification.” Remote Sensing, Vol. 12(No. 4):pp. 655. pp doi:10.3390/rs12040655.
  • Hinton, G.E., Osindero, S., and Teh, Y.W. 2006. “A fast learning algorithm for deep belief nets.” Neural Computation, Vol. 18(No. 7): pp. 1527–1554. doi:10.1162/NECO.2006.18.7.1527.
  • Hong, G., Wang, S., Li, J., and Huang, J. 2015. “Fully polarimetric synthetic aperture radar (SAR) processing for crop type identification.” Photogrammetric Engineering & Remote Sensing, Vol. 81(No. 2): pp. 109–117. doi:10.14358/PERS.81.2.109.
  • Hong, W., Shao, L., and Yin, Q. 2017. "Decision hierarchical classification by FLD for vegetation application using PolSAR features." IEEE International Geoscience & Remote Sensing Symposium, Fort Worth, TX, July 23–28, 2017.
  • Hua, W.Q., and Guo, Y.H. 2020. “Classification of polarimetric synthetic aperture radar images based on multilayer wishart-restricted Boltzmann machine.” Journal of Applied Remote Sensing, Vol. 14(No. 03): pp. 1–13. doi:10.1117/1.JRS.14.036516.
  • Jamali, A., Mahdianpari, M., Mohammadimanesh, F., Bhattacharya, A., and Homayouni, S. 2022. “PolSAR image classification based on deep convolutional neural networks using wavelet transformation.” IEEE Geoscience and Remote Sensing Letters, Vol. 19(No. No. 2022): pp. 1–5. doi:10.1109/LGRS.2022.3185118.
  • Jiao, L.C., and Liu, F. 2016. “Wishart deep stacking network for fast PolSAR image classification.” IEEE Transactions on Image Processing, Vol. 25(No. 7): pp. 3273–3286. doi:10.1109/TIP.2016.2567069.
  • Jun-Feng, G.E., and., and Luo, Y.-P. 2009. “A comprehensive study for asymmetric AdaBoost and its application in object detection.” Acta Automatica Sinica, Vol. 35(No. 11): pp. 1403–1409. doi:10.1016/S1874-1029(08)60115-9.
  • Khosravi, I., Razoumny, Y., Afkoueieh, J.H., and Alavipanah, S.K. 2021. “Fully polarimetric synthetic aperture radar data classification using probabilistic and non-probabilistic kernel methods.” European Journal of Remote Sensing, Vol. 54(No. 1): pp. 310–317. doi:10.1080/22797254.2021.1924081.
  • 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, Vol. 15(No. 11): pp. 2299–2311. doi:10.1080/01431169408954244.
  • Lee, J. S., and Pottier, E. 2009. Polarimetric Radar Imaging: From Basics to Applications. Boca Raton: CRC press.
  • Liu, S.J., Luo, H.W., and Shi, Q. 2021. “Active ensemble deep learning for polarimetric synthetic aperture radar image classification.” IEEE Geoscience and Remote Sensing Letters, Vol. 18(No. 9): pp. 1580–1584. doi:10.1109/LGRS.2020.3005076.
  • Maghsoudi, Y., Collins, M., and Leckie, D.G. 2012. “Polarimetric classification of boreal forest using nonparametric feature selection and multiple classifiers.” International Journal of Applied Earth Observation and Geoinformation, Vol. 19(No. No. 2012): pp. 139–150. doi:10.1016/j.jag.2012.04.015.
  • Mangai, U.G., Samanta, S., Das, S., and Chowdhury, P.R. 2010. “A survey of decision fusion and feature fusion strategies for pattern classification.” IETE Technical Review, Vol. 27(No. 4): pp. 293–307. doi:10.4103/0256-4602.64604.
  • Mountrakis, G., Im, J., and Ogole, C. 2011. “Support vector machines in remote sensing: A review.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66(No. 3): pp. 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
  • Polikar, R. 2006. “Essemble based systems in decision making.” IEEE Circuits and Systems Magazine, Vol. 6(No. 3): pp. 21–45. doi:10.1109/MCAS.2006.1688199.
  • Qi, Z.X., Yeh, A.G.O., Li, X., and Lin, Z. 2012. “A novel algorithm for land use and land cover classification using radarsat-2 polarimetric SAR data.” Remote Sensing of Environment, Vol. 118(No. 3): pp. 21–39. doi:10.1016/j.rse.2011.11.001.
  • Qin, F., Guo, J., and Sun, W. 2017. “Object-oriented ensemble classification for polarimetric SAR imagery using restricted Boltzmann machines.” Remote Sensing Letters, Vol. 8(No. 3): pp. 204–213. doi:10.1080/2150704X.2016.1258128.
  • Radman, A., Mahdianpari, M., Brisco, B., Salehi, B., and Mohammadimanesh, F. 2022. “Dual-branch fusion of convolutional neural network and graph convolutional network for PolSAR image classification.” Remote Sensing, Vol. 15(No. 1): pp. 75. doi:10.3390/rs15010075.
  • Santana-Cedres, D., Gomez, L., Trujillo, A., Aleman-Flores, M., Deriche, R., and Alvarez, L. 2019. “Supervised classification of fully PolSAR images using active contour models.” IEEE Geoscience and Remote Sensing Letters, Vol. 16(No. 7): pp. 1165–1169. doi:10.1109/LGRS.2019.2892524.
  • Shang, R.H., Liu, Y.K., Wang, J.M., Jiao, L.C., and Stolkin, R. 2019. “Stacked auto-encoder for classification of polarimetric SAR images based on scattering energy.” International Journal of Remote Sensing, Vol. 40(No. 13): pp. 5094–5120. doi:10.1080/01431161.2019.1579378.
  • Shokrollahi, M., and Ebadi, H. 2016. “Improving the accuracy of land cover classification using fusion of polarimetric SAR and hyperspectral images.” Journal of the Indian Society of Remote Sensing, Vol. 44(No. 6): pp. 1017–1024. doi:10.1007/s12524-016-0559-4.
  • Tessier, N., Boissonnot, R., Desvignes, V., Fröchen, M., Merlo, M., Blanchard, O., Chevrier, C., et al. 2023. “Use and storage of pesticides at home in France (the pesti’home survey 2014).” Environmental Research, Vol. 216(No. Pt 2): pp. 114452. doi:10.1016/j.envres.2022.114452.
  • Van Zyl, J.J. 1989. “Unsupervised classification of scattering behavior using radar polarimetry data.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 27(No. 1): pp. 36–45. doi:10.1109/36.20273.
  • Wang, J.L., Hou, B., Ren, B., Zhang, Y.K., Yang, M.J., Wang, S., and Jiao, L.C. 2022. “Parameter selection of Touzi decomposition and a distribution improved autoencoder for PolSAR image classification.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 186(No. 2022): pp. 246–266. doi:10.1016/j.isprsjprs.2022.02.003.
  • Yin, Q., Cheng, J.D., Zhang, F., Zhou, Y.S., Shao, L.Y., and Hong, W. 2020. “Interpretable PolSAR image classification based on adaptive-dimension feature space decision tree.” IEEE Access., Vol. 8(No. No. 2020): pp. 173826–173837. doi:10.1109/ACCESS.2020.3023134.
  • Zhang, L., Zhang, S., Zou, B., and Dong, H. 2022. “Unsupervised deep representation learning and few-shot classification of PolSAR images.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60(No. 2020): pp. 1–16. doi:10.1109/TGRS.2020.3043191.
  • Zhao, M., Cheng, Y., Qin, X., Yu, W., and Wang, P. 2023. “Semi-supervised classification of PolSAR images based on co-training of CNN and SVM with limited labelled samples.” Sensors, Vol. 23(No. 4): pp. 2109. doi:10.3390/s23042109.
  • Zhu, L.K., Ma, X.S., Wu, P.H., and Xu, J.G. 2021. “Multiple classifiers based semi-supervised polarimetric SAR image classification method.” Sensors, Vol. 21(No. 9): pp. 3006. doi:10.3390/s21093006.