Semantic segmentation of PolSAR image data using advanced deep learning model

  • 1.

    Elachi, C., Bicknell, T., Jordan, R. L. & Wu, C. Spaceborne synthetic-aperture imaging radars: applications, techniques, and technology. Proc. IEEE 70, 1174–1209 (1982).

    Article 

    Google Scholar
     

  • 2.

    Singh, A., Meena, G. K., Kumar, S. & Gaurav, K. Evaluation of the penetration depth of L- and S-band (NISAR mission) microwave SAR signals into ground. In: 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC) 1 (2019). https://doi.org/10.23919/URSIAP-RASC.2019.8738217.

  • 3.

    van Zyl, J. J. Unsupervised classification of scattering behavior using radar polarimetry data. IEEE Trans. Geosci. Remote Sens. 27, 36–45 (1989).

    ADS 
    Article 

    Google Scholar
     

  • 4.

    Gomez-Chova, L. et al. Urban monitoring using multi-temporal SAR and multi-spectral data. Pattern Recognit. Lett. 27, 234–243 (2006).

    ADS 
    Article 

    Google Scholar
     

  • 5.

    Khoshboresh-Masouleh, M., Alidoost, F. & Arefi, H. Multiscale building segmentation based on deep learning for remote sensing RGB images from different sensors. J. Appl. Remote Sens. 14, 1–21 (2020).

    Article 

    Google Scholar
     

  • 6.

    Wang, X., Cao, Z., Cui, Z., Liu, N. & Pi, Y. PolSAR image classification based on deep polarimetric feature and contextual information. J. Appl. Remote Sens. 13, 1–17 (2019).

    ADS 

    Google Scholar
     

  • 7.

    Gupta, S., Singh, D., Singh, K. P. & Kumar, S. An efficient use of random forest technique for SAR data classification. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3286–3289 (2015). https://doi.org/10.1109/IGARSS.2015.7326520.

  • 8.

    Camargo, F. F., Sano, E. E., Almeida, C. M., Mura, J. C. & Almeida, T. A comparative assessment of machine-learning techniques for land use and land cover classification of the brazilian tropical savanna using ALOS-2/PALSAR-2 polarimetric images. Remote Sens. 11, 1600 (2019).

    ADS 
    Article 

    Google Scholar
     

  • 9.

    Lapini, A. et al. Comparison of machine learning methods applied to SAR images for forest classification in mediterranean areas. Remote Sens. 12, 369 (2020).

    ADS 
    Article 

    Google Scholar
     

  • 10.

    Geng, J., Wang, H., Fan, J. & Ma, X. Deep supervised and contractive neural network for SAR image classification. IEEE Trans. Geosci. Remote Sens. 55, 2442–2459 (2017).

    ADS 
    Article 

    Google Scholar
     

  • 11.

    He, C., Zhuo, T., Zhao, S., Yin, S. & Chen, D. Particle filter sample texton feature for SAR image classification. IEEE Geosci. Remote Sens. Lett. 12, 1141–1145 (2015).

    ADS 
    Article 

    Google Scholar
     

  • 12.

    Planins̆ic̆, P., Singh, J. & Gleich, D. SAR Image categorization using parametric and nonparametric approaches within a dual tree CWT. IEEE Geosci. Remote Sens. Lett. 11, 1757–1761 (2014).

    ADS 
    Article 

    Google Scholar
     

  • 13.

    Jia, S., Shen, L. & Li, Q. Gabor feature-based collaborative representation for hyperspectral imagery classification. IEEE Trans. Geosci. Remote Sens. 53, 1118–1129 (2015).

    ADS 
    Article 

    Google Scholar
     

  • 14.

    De-yong, H., Xiao-juan, L., Wen-ji, Z. & Hui-li, G. Texture analysis and its application for single-band SAR thematic information extraction. In: IGARSS 2008 – 2008 IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp. II-935–II-938 (2008).

  • 15.

    Dai, D., Yang, W. & Sun, H. Multilevel local pattern histogram for SAR image classification. IEEE Geosci. Remote Sens. Lett. 8, 225–229 (2011).

    ADS 
    Article 

    Google Scholar
     

  • 16.

    Su, X., He, C., Feng, Q., Deng, X. & Sun, H. A Supervised classification method based on conditional random fields with multiscale region connection calculus model for SAR image. IEEE Geosci. Remote Sens. Lett. 8, 497–501 (2011).

    ADS 
    Article 

    Google Scholar
     

  • 17.

    Chust, G., Ducrot, D. & Pretus, J. L. L. Land cover discrimination potential of radar multitemporal series and optical multispectral images in a Mediterranean cultural landscape. Int. J. Remote Sens. 25, 3513–3528 (2004).

    ADS 
    Article 

    Google Scholar
     

  • 18.

    Dibs, H., Hasab, H. A., Al-Rifaie, J. K. & Al-Ansari, N. An optimal approach for land-use/land-cover mapping by integration and fusion of multispectral landsat OLI images: case study in Baghdad. Iraq. Water Air Soil Pollut. 231, 488 (2020).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 19.

    Li, X., Ling, F., Foody, G. M. & Du, Y. A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions. IEEE Trans. Geosci. Remote Sens. 54, 3822–3841 (2016).

    ADS 
    Article 

    Google Scholar
     

  • 20.

    Xu, Y. et al. Advanced multi-sensor optical remote sensing for urban land use and land cover classification: outcome of the 2018 IEEE GRSS data fusion contest. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 12, 1709–1724 (2019).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 21.

    Alaska Satellite Facility. What is SAR? (2021). https://asf.alaska.edu/information/sar-information/what-is-sar/.

  • 22.

    Kumar, S., Garg, R. D., Govil, H. & Kushwaha, S. P. S. PolSAR-decomposition-based extended water cloud modeling for forest aboveground biomass estimation. Remote Sens. 11, 1–27 (2019).


    Google Scholar
     

  • 23.

    Cloude, S. R. & Pottier, E. A review of target decomposition theorems in radar polarimetry. IEEE Trans. Geosci. Remote Sens. 34, 498–518 (1996).

    ADS 
    Article 

    Google Scholar
     

  • 24.

    M E, B. P. & Kumar, S. PolInSAR decorrelation-based decomposition modelling of spaceborne multifrequency SAR data. Int. J. Remote Sens. 42, 1398–1419 (2021).

  • 25.

    Yamaguchi, Y., Yajima, Y. & Yamada, H. A four-component decomposition of POLSAR images based on the coherency matrix. IEEE Geosci. Remote Sens. Lett. 3, 292–296 (2006).

    ADS 
    Article 

    Google Scholar
     

  • 26.

    Lee, J.-S. & Pottier, E. Polarimetric Radar Imaging From Basics to Applications (CRC Press, 2009).


    Google Scholar
     

  • 27.

    Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967).

    MATH 
    Article 

    Google Scholar
     

  • 28.

    Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).

    MATH 
    Article 

    Google Scholar
     

  • 29.

    Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J. & Scholkopf, B. Support vector machines. IEEE Intell. Syst. their Appl. 13, 18–28 (1998).

    Article 

    Google Scholar
     

  • 30.

    Salakhutdinov, R. & Statistics, G. H. B. T.-P. of the T. I. C. on A. I. and. Deep Boltzmann Machines. 448–455 (2009).

  • 31.

    LeCun, Y., Kavukcuoglu, K. & Farabet, C. Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 253–256 (2010). https://doi.org/10.1109/ISCAS.2010.5537907.

  • 32.

    Chen, X., Xiang, S., Liu, C. & Pan, C. Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 11, 1797–1801 (2014).

    ADS 
    Article 

    Google Scholar
     

  • 33.

    Hinton, G. E., Osindero, S. & Teh, Y.-W. A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006).

    MathSciNet 
    PubMed 
    MATH 
    Article 

    Google Scholar
     

  • 34.

    Ma, X., Geng, J. & Wang, H. Hyperspectral image classification via contextual deep learning. EURASIP J. Image Video Process. 2015, 20 (2015).

    Article 

    Google Scholar
     

  • 35.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 36.

    Liu, C., Yin, J., Yang, J. Application of deep learning to polarimetric SAR classification. In: IET International Radar Conference 2015, pp. 1–4 (2015). https://doi.org/10.1049/cp.2015.1182.

  • 37.

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

    Article 

    Google Scholar
     

  • 38.

    Lv, Q. et al. Urban land use and land cover classification using remotely sensed SAR data through deep belief networks. J. Sens. 2015, 538063 (2015).

    Article 

    Google Scholar
     

  • 39.

    Liu, F., Jiao, L., Hou, B. & Yang, S. POL-SAR image classification based on wishart DBN and local spatial information. IEEE Trans. Geosci. Remote Sens. 54, 3292–3308 (2016).

    ADS 
    Article 

    Google Scholar
     

  • 40.

    Gong, M., Zhao, J., Liu, J., Miao, Q. & Jiao, L. Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 27, 125–138 (2016).

    MathSciNet 
    PubMed 
    Article 

    Google Scholar
     

  • 41.

    Chen, S., Wang, H., Xu, F. & Jin, Y. Target classification using the deep convolutional networks for SAR images. IEEE Trans. Geosci. Remote Sens. 54, 4806–4817 (2016).

    ADS 
    Article 

    Google Scholar
     

  • 42.

    Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H. Encoder–decoder with atrous separable convolution for semantic image segmentation. In: Computer Vision and Pattern Recognition (2018).

  • 43.

    Russakovsky, O. et al. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015).

    MathSciNet 
    Article 

    Google Scholar
     

  • 44.

    Kumar, A., Garg, R., Prateek, M. & Kumar, S. Implementation of Evolutionary Computing Algorithm for Polarimetric SAR Data Processing and Classification (TEC-07): L&S band Airborne SAR Research Announcement (RA) project under the NASA-ISRO Synthetic Aperture Radar (NISAR) mission. (Space Applications Centre, Indian Space Research Organisation, Ahmedabad, 2021).

  • 45.

    Wood, J. W., White, R. G. & Oliver, C. J. Distortion free SAR imagery and change detection. In: Proceedings of the 1988 IEEE National Radar Conference, pp. 95–99 (1988). https://doi.org/10.1109/NRC.1988.10937.

  • 46.

    Freeman, A. SAR calibration: an overview. IEEE Trans. Geosci. Remote Sens. 30, 1107–1121 (1992).

    ADS 
    Article 

    Google Scholar
     

  • 47.

    Touzi, R., Hawkins, R. K. & Cote, S. High-precision assessment and calibration of polarimetric RADARSAT-2 SAR using transponder measurements. IEEE Trans. Geosci. Remote Sens. 51, 487–503 (2013).

    ADS 
    Article 

    Google Scholar
     

  • 48.

    Wang, F., Liu, A., Xu, H. & Jiang, T. A Method for estimating and validating polarimetric distortion parameters using corner reflectors and its applicability analysis. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 12, 5345–5359 (2019).

    ADS 
    Article 

    Google Scholar
     

  • 49.

    Agrawal, S., Raghavendra, S. & Kumar, S. Geospatial data for the himalayan region: requirements, availability, and challenges. In Remote Sensing of Northwest Himalayan Ecosystems (eds Navalgund, R. R. & Kumar, A. S.) 471–500 (Springer, Singapore, 2018).


    Google Scholar
     

  • 50.

    Richards, J. A. Remote Sensing with Imaging Radar (Springer, 2009).

    Book 

    Google Scholar
     

  • 51.

    Woodhouse, I.F. Introduction to Microwave Remote Sensing. (CRC Press, Boca Raton, 2006). https://doi.org/10.1201/9781315272573.

  • 52.

    El-Darymli, K., McGuire, P., Gill, E., Power, D. & Moloney, C. Understanding the significance of radiometric calibration for synthetic aperture radar imagery. In: 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–6 (2014). https://doi.org/10.1109/CCECE.2014.6901104.

  • 53.

    Yang, J., Qiu, X., Ding, C. & Lei, B. Identification of stable backscattering features, suitable for maintaining absolute synthetic aperture radar (SAR) radiometric calibration of sentinel-1. Remote Sens. 10, 1010 (2018).

    ADS 
    Article 

    Google Scholar
     

  • 54.

    Loew, A. & Mauser, W. Generation of geometrically and radiometrically terrain corrected SAR image products. Remote Sens. Environ. 106, 337–349 (2007).

    ADS 
    Article 

    Google Scholar
     

  • 55.

    Huang, L., Li, Z. & Tian, B. Local incidence angle referenced classification on polarimetric synthetic aperture radar images in mountain glacier areas. J. Appl. Remote Sens. 10, 1–14 (2016).

    ADS 

    Google Scholar
     

  • 56.

    Warner, T., Bell, R. & Singhroy, V. Local incidence angle effects on X- and C-band radar backscatter of boreal forest communities. Can. J. Remote Sens. 22, 269–279 (1996).

    ADS 
    Article 

    Google Scholar
     

  • 57.

    Shibayama, T., Yamaguchi, Y. & Yamada, H. Polarimetric scattering properties of landslides in forested areas and the dependence on the local incidence angle. Remote Sens. 7, 15424–15442 (2015).

    ADS 
    Article 

    Google Scholar
     

  • 58.

    Hu, R., Rao, B. S. M. R., Alaee-Kerahroodi, M. & Ottersten, B. Orthorectified polar format algorithm for generalized spotlight SAR imaging with DEM. IEEE Trans. Geosci. Remote Sens. 59, 3999–4007 (2021).

    ADS 
    Article 

    Google Scholar
     

  • 59.

    Small, D. & Schubert, A. Guide to ASAR Geocoding (University of Zürich, 2008).


    Google Scholar
     

  • 60.

    Chen, X., Sun, Q. & Hu, J. Generation of complete SAR geometric distortion maps based on DEM and neighbor gradient algorithm. Appl. Sci. 8, 2206 (2018).

    Article 

    Google Scholar
     

  • 61.

    Cigna, F., Bateson, L. B., Jordan, C. J. & Dashwood, C. Simulating SAR geometric distortions and predicting Persistent Scatterer densities for ERS-1/2 and ENVISAT C-band SAR and InSAR applications: Nationwide feasibility assessment to monitor the landmass of Great Britain with SAR imagery. Remote Sens. Environ. 152, 441–466 (2014).

    ADS 
    Article 

    Google Scholar
     

  • 62.

    Kumar, S. et al. Polarimetric calibration of spaceborne and airborne multifrequency SAR data for scattering-based characterization of manmade and natural features. Adv. Space Res. https://doi.org/10.1016/j.asr.2021.02.023 (2021).

    Article 

    Google Scholar
     

  • 63.

    Freeman, A. & Durden, S. L. A three-component scattering model for polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 36, 963–973 (1998).

    ADS 
    Article 

    Google Scholar
     

  • 64.

    Shafai, S. S. & Kumar, S. PolInSAR coherence and entropy-based hybrid decomposition mode. Earth Space Sci. 7, 1–17 (2020).

    Article 

    Google Scholar
     

  • 65.

    Van, Z. J. & Kim, Y. Synthetic Aperture Radar Polarimetry (Wiley, 2011).


    Google Scholar
     

  • 66.

    Yamaguchi, Y., Sato, A., Boerner, W., Sato, R. & Yamada, H. Four-component scattering power decomposition with rotation of coherency matrix. IEEE Trans. Geosci. Remote Sens. 49, 2251–2258 (2011).

    ADS 
    Article 

    Google Scholar
     

  • 67.

    Sarabandi, K., Pierce, L. E. & Ulaby, F. T. Calibration of a polarimetric imaging SAR. IEEE Trans. Geosci. Remote Sens. 30, 540–549 (1992).

    ADS 
    Article 

    Google Scholar
     

  • 68.

    Sun, G., Huang, L., Chen, K. & Han, C. An efficient polarimetric SAR calibration algorithm using corner reflectors. Can. J. Remote Sens. 43, 286–296 (2017).

    ADS 
    Article 

    Google Scholar
     

  • 69.

    Jung, Y. T. & Park, S.-E. Comparative analysis of polarimetric SAR calibration methods. Remote Sens. 10, 2060 (2018).

    ADS 
    Article 

    Google Scholar
     

  • 70.

    Villa, A., Iannini, L., Giudici, D., Monti-Guarnieri, A. & Tebaldini, S. Calibration of SAR Polarimetric images by means of a covariance matching approach. IEEE Trans. Geosci. Remote Sens. 53, 674–686 (2015).

    ADS 
    Article 

    Google Scholar
     

  • 71.

    Kim, J. S., Papathanassiou, K. P., Scheiber, R. & Quegan, S. Correcting distortion of polarimetric SAR data induced by ionospheric scintillation. IEEE Trans. Geosci. Remote Sens. 53, 6319–6335 (2015).

    ADS 
    Article 

    Google Scholar
     

  • 72.

    Maiti, A., Kumar, S., Tolpekin, V. & Agrawal, S. A computationally efficient hybrid framework for polarimetric calibration of quad-pol SAR data. Earth Space Sci. 8, 1–22 (2021).

    Article 

    Google Scholar
     

  • 73.

    Li, L., Zhu, Y., Hong, J., Ming, F. & Wang, Y. Design and implementation of a novel polarimetric active radar calibrator for Gaofen-3 SAR. Sensors 18, 2620 (2018).

    ADS 
    PubMed Central 
    Article 
    PubMed 

    Google Scholar
     

  • 74.

    Shimada, M. Model-based polarimetric SAR calibration method using forest and surface scattering targets. In: 2011 IEEE International Geoscience and Remote Sensing Symposium, pp. 3736–3739 (2011). https://doi.org/10.1109/IGARSS.2011.6050037.

  • 75.

    Yamaguchi, Y. Polarimetric SAR Imaging Theory and Applications (CRC Press, 2020).

    Book 

    Google Scholar
     

  • 76.

    Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). (2021). https://uavsar.jpl.nasa.gov/cgi-bin/data.pl.

  • 77.

    Yamaguchi, Y., Moriyama, T., Ishido, M. & Yamada, H. Four-component scattering model for polarimetric SAR image decomposition. IEEE Trans. Geosci. Remote Sens. 43, 1699–1706 (2005).

    ADS 
    Article 

    Google Scholar
     

  • 78.

    Singh, G., Yamaguchi, Y. & Park, S. General four-component scattering power decomposition with unitary transformation of coherency matrix. IEEE Trans. Geosci. Remote Sens. 51, 3014–3022 (2013).

    ADS 
    Article 

    Google Scholar
     

  • 79.

    Gabor, D. Theory of communication. Part 1: the analysis of information. J. Inst. Electr. Eng.—Part III Radio Commun. Eng. 93, 429–441 (1946).

  • 80.

    Deng, G. & Cahill, L. W. An adaptive Gaussian filter for noise reduction and edge detection. In: 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, vol. 3, pp. 1615–1619 (1993). https://doi.org/10.1109/NSSMIC.1993.373563.

  • 81.

    Dong, Y., Li, M. & Li, J. Image retrieval based on improved Canny edge detection algorithm. In: Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), pp. 1453–1457 (2013). https://doi.org/10.1109/MEC.2013.6885296.

  • 82.

    Canny, J. A Computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986).

    Article 

    Google Scholar
     

  • 83.

    Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).

    MATH 

    Google Scholar
     

  • 84.

    Dong, Y., Du, B. & Zhang, L. Target detection based on random forest metric learning. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 1830–1838 (2015).

  • 85.

    Shelhamer, E., Long, J. & Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2017).

    PubMed 
    Article 

    Google Scholar
     

  • 86.

    Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation BT. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. in (eds. Navab, N., Hornegger, J., Wells, W. M. & Frangi, A. F.) 234–241 (Springer, 2015).

  • 87.

    Badrinarayanan, V., Kendall, A. & Cipolla, R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2481–2495 (2017).

    PubMed 
    Article 

    Google Scholar
     

  • 88.

    Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A. L. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40, 834–848 (2018).

    PubMed 
    Article 

    Google Scholar
     

  • 89.

    Holschneider, M., Kronland-Martinet, R., Morlet, J. & Tchamitchian, P. A Real-Time Algorithm for Signal Analysis with the Help of the Wavelet Transform. (Springer, Berlin, 1990). https://doi.org/10.1007/978-3-642-75988-8_28.

  • 90.

    Chollet, F. Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807 (2017). https://doi.org/10.1109/CVPR.2017.195.

  • 91.

    Chen, L.-C., Papandreou, G., Schroff, F. & Adam, H. Rethinking atrous convolution for semantic image segmentation. in Computer Vision and Pattern Recognition (2017).

  • 92.

    Rebentrost, P., Mohseni, M. & Lloyd, S. Quantum support vector machine for big data classification. Phys. Rev. Lett. 113, 130503 (2014).

    ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar
     

  • 93.

    Hensley, S. et al. The UAVSAR instrument: Description and first results. In: 2008 IEEE Radar Conference, pp. 1–6 (2008). https://doi.org/10.1109/RADAR.2008.4720722.

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