Elachi, C., Bicknell, T., Jordan, R. L. & Wu, C. Spaceborne synthetic-aperture imaging radars: applications, techniques, and technology. Proc. IEEE 70, 1174–1209 (1982).
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.
van Zyl, J. J. Unsupervised classification of scattering behavior using radar polarimetry data. IEEE Trans. Geosci. Remote Sens. 27, 36–45 (1989).
Gomez-Chova, L. et al. Urban monitoring using multi-temporal SAR and multi-spectral data. Pattern Recognit. Lett. 27, 234–243 (2006).
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).
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).
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.
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).
Lapini, A. et al. Comparison of machine learning methods applied to SAR images for forest classification in mediterranean areas. Remote Sens. 12, 369 (2020).
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).
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).
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).
Jia, S., Shen, L. & Li, Q. Gabor feature-based collaborative representation for hyperspectral imagery classification. IEEE Trans. Geosci. Remote Sens. 53, 1118–1129 (2015).
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).
Dai, D., Yang, W. & Sun, H. Multilevel local pattern histogram for SAR image classification. IEEE Geosci. Remote Sens. Lett. 8, 225–229 (2011).
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).
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).
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).
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).
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).
Alaska Satellite Facility. What is SAR? (2021). https://asf.alaska.edu/information/sar-information/what-is-sar/.
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).
Cloude, S. R. & Pottier, E. A review of target decomposition theorems in radar polarimetry. IEEE Trans. Geosci. Remote Sens. 34, 498–518 (1996).
M E, B. P. & Kumar, S. PolInSAR decorrelation-based decomposition modelling of spaceborne multifrequency SAR data. Int. J. Remote Sens. 42, 1398–1419 (2021).
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).
Lee, J.-S. & Pottier, E. Polarimetric Radar Imaging From Basics to Applications (CRC Press, 2009).
Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967).
Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J. & Scholkopf, B. Support vector machines. IEEE Intell. Syst. their Appl. 13, 18–28 (1998).
Salakhutdinov, R. & Statistics, G. H. B. T.-P. of the T. I. C. on A. I. and. Deep Boltzmann Machines. 448–455 (2009).
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.
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).
Hinton, G. E., Osindero, S. & Teh, Y.-W. A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006).
Ma, X., Geng, J. & Wang, H. Hyperspectral image classification via contextual deep learning. EURASIP J. Image Video Process. 2015, 20 (2015).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
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.
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).
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).
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).
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).
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).
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).
Russakovsky, O. et al. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015).
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).
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.
Freeman, A. SAR calibration: an overview. IEEE Trans. Geosci. Remote Sens. 30, 1107–1121 (1992).
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).
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).
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).
Richards, J. A. Remote Sensing with Imaging Radar (Springer, 2009).
Woodhouse, I.F. Introduction to Microwave Remote Sensing. (CRC Press, Boca Raton, 2006). https://doi.org/10.1201/9781315272573.
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.
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).
Loew, A. & Mauser, W. Generation of geometrically and radiometrically terrain corrected SAR image products. Remote Sens. Environ. 106, 337–349 (2007).
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).
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).
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).
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).
Small, D. & Schubert, A. Guide to ASAR Geocoding (University of Zürich, 2008).
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).
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).
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).
Freeman, A. & Durden, S. L. A three-component scattering model for polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 36, 963–973 (1998).
Shafai, S. S. & Kumar, S. PolInSAR coherence and entropy-based hybrid decomposition mode. Earth Space Sci. 7, 1–17 (2020).
Van, Z. J. & Kim, Y. Synthetic Aperture Radar Polarimetry (Wiley, 2011).
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).
Sarabandi, K., Pierce, L. E. & Ulaby, F. T. Calibration of a polarimetric imaging SAR. IEEE Trans. Geosci. Remote Sens. 30, 540–549 (1992).
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).
Jung, Y. T. & Park, S.-E. Comparative analysis of polarimetric SAR calibration methods. Remote Sens. 10, 2060 (2018).
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).
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).
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).
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).
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.
Yamaguchi, Y. Polarimetric SAR Imaging Theory and Applications (CRC Press, 2020).
Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). (2021). https://uavsar.jpl.nasa.gov/cgi-bin/data.pl.
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).
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).
Gabor, D. Theory of communication. Part 1: the analysis of information. J. Inst. Electr. Eng.—Part III Radio Commun. Eng. 93, 429–441 (1946).
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.
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.
Canny, J. A Computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986).
Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).
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).
Shelhamer, E., Long, J. & Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2017).
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).
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).
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).
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.
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.
Chen, L.-C., Papandreou, G., Schroff, F. & Adam, H. Rethinking atrous convolution for semantic image segmentation. in Computer Vision and Pattern Recognition (2017).
Rebentrost, P., Mohseni, M. & Lloyd, S. Quantum support vector machine for big data classification. Phys. Rev. Lett. 113, 130503 (2014).
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.