Publications about 'compressed sensing'

Articles in journal or book chapters

1. Meenu Rani, S. B. Dhok, and R. B. Deshmukh. A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications. IEEE Access, 6:4875-4894, 2018. Keyword(s): Compressive Sensing, CS, Sensors, Transforms, Mathematical model, Sparse matrices, Compressed sensing, Reconstruction algorithms, Image reconstruction, systematic review, compressive sensing, sensing modality, sparse representation, compressible representation, Nyquist sampling rate, varied reconstruction algorithms, compressive measurements, CS acquisition strategies, signal representation, transform domain, sparsity, random demodulator, CS reconstruction algorithms, OMP, CS applications. [Abstract] [bibtex-entry]



2. O. Ponce, P. Prats-Iraola, R. Scheiber, A. Reigber, A. Moreira, and E. Aguilera. Polarimetric 3-D Reconstruction From Multicircular SAR at P-Band. IEEE_J_GRSL, 11(4):803-807, April 2014. Keyword(s): airborne radar, array signal processing, calibration, compressed sensing, image reconstruction, image resolution, optical focusing, radar imaging, radar polarimetry, radar resolution, singular value decomposition, synthetic aperture radar, 2D image stacking, 3D cone-shaped sidelobe suppression, 3D reconstruction polarimetry, BF, CS, DLR, FFBP, German Aerospace Center F-SAR system, LOS, P-band, SVD, Vordemwald Switzerland, beamforming, circular flight, complex reflectivity solution, compressive sensing, constant phase offset estimation, fast-factorized back projection, line of sight, multicircular synthetic aperture radar, phase calibration method, polarimetric MCSAR airborne experiment, Apertures, Calibration, Focusing, Image resolution, Remote sensing, Synthetic aperture radar, Circular synthetic aperture radar (SAR) (CSAR), compressive sensing (CS), phase calibration, polarimetric SAR (PolSAR), singular value decomposition (SVD), tomography. [bibtex-entry]



3. Zhu. Xiao Xiang and Richard Bamler. Superresolving SAR Tomography for Multidimensional Imaging of Urban Areas: Compressive sensing-based TomoSAR inversion. IEEE Signal Processing Magazine, 31(4):51-58, July 2014. Keyword(s): SAR Processing, SAR Tomography, PSI, Persistent Scatterer Interferometry, Deformation Monitoring, Deformation, compressed sensing, image motion analysis, image reconstruction, radar imaging, radar resolution, synthetic aperture radar, tomography, 4D point clouds, LiDAR, VHR spaceborne tomographic SAR inversion, building deformation, compressive sensing-based TomoSAR inversion, dynamic city model reconstruction, motion reconstruction, multicomponent nonlinear motion, multidimensional imaging, multiple scatterers, point density, shape reconstruction, synthetic aperture radar, urban areas, urban infrastructure, very high spatial resolution SAR tomography, Mathematical model, Signal processing algorithms, Signal resolution, Signal to noise ratio, Synthetic aperture radar, Tomography, Urban areas. [Abstract] [bibtex-entry]



4. M. \cCetin, I. Stojanovic, N.O. Önhon, K.R. Varshney, S. Samadi, W.C. Karl, and A.S. Willsky. Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing. IEEE Signal Processing Magazine, 31(4):27-40, July 2014. Keyword(s): SAR Processing, Autofocus, compressed sensing, image representation, radar imaging, synthetic aperture radar, SAR image formation, SAR sensing mission design, anisotropy characterization, compressed sensing-based analysis, joint autofocusing, joint imaging, phase errors, sparsity-based methods, sparsity-driven synthetic aperture radar imaging, synthesis-based sparse signal representation formulations, wide-angle SAR imaging, Image reconstruction, Imaging, Radar imaging, Radar polarimetry, Scattering, Synthetic aperture radar. [Abstract] [bibtex-entry]



5. Esteban Aguilera, Matteo Nannini, and Andreas Reigber. A Data-Adaptive Compressed Sensing Approach to Polarimetric SAR Tomography of Forested Areas. IEEE Geosci. Remote Sens. Lett., 10(3):543-547, May 2013. Keyword(s): SAR Processing, SAR Tomography, Compressed sensing, Compressive sensing, Covariance matrix, Synthetic aperture radar, Tomography, data compression, forestry, radar polarimetry, tomography, vegetation mapping, wavelet transforms, DLR, E-SAR sensor, German Aerospace Center, covariance fitting, data adaptive compressed sensing approach, data adaptive orthonormal basis, forested areas, fully polarimetric L-band data, multiple looks, multiple polarimetric channels, partial scatterers, polarimetric SAR tomography, superresolution imaging, Distributed compressed sensing (DCS), Kronecker basis, polarimetry, wavelets, E-SAR, F-SAR, L-Band, Airborne SAR. [Abstract] [bibtex-entry]



6. Esteban Aguilera, Matteo Nannini, and Andreas Reigber. Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas. IEEE Trans. Geosci. Remote Sens., 51(12):5283-5295, December 2013. Keyword(s): SAR Processing, SAR Tomography, Covariance matrix, Estimation, Power distribution, Sensors, Synthetic aperture radar, Tomography, Wavelet transforms, Compressed sensing (CS), compressive sensing, CS, forest structure, synthetic aperture radar (SAR) tomography, wavelets, E-SAR, F-SAR, L-Band, Airborne SAR. [Abstract] [bibtex-entry]



7. Esteban Aguilera, Matteo Nannini, and Andreas Reigber. Multisignal Compressed Sensing for Polarimetric SAR Tomography. IEEE Geosci. Remote Sens. Lett., 5(9):871-875, September 2012. Keyword(s): SAR Processing, SAR Tomography, Tomography, Compressed sensing, Remote sensing, Sensors, Tomography, Vectors, compressed sensing, compressive sensing, CS, geometry, image reconstruction, image sensors, radar imaging, radar polarimetry, synthetic aperture radar, tomography, 3D imaging, DCS, E-SAR sensor, German Aerospace Center, azimuth-range pixel, data collection processing, distributed compressed sensing, multisignal compressed sensing, parallel track, polarimetric L-band data, polarimetric SAR sensor, polarimetric SAR tomography, polarimetric channel, polarimetric synthetic aperture radar sensor, repeat-pass acquisition geometry, signal reconstruction, temporal baseline, tomographic signal, vertical reflectivity function, Compressed sensing (CS), distributed compressed sensing (DCS), polarimetry, synthetic aperture radar (SAR) tomography, L-band, E-SAR, F-SAR. [Abstract] [bibtex-entry]



8. S. Xilong, Y. Anxi, D. Zhen, and L. Diannong. Three-Dimensional SAR Focusing via Compressive Sensing: The Case Study of Angel Stadium. IEEE Geosci. Remote Sens. Lett., PP(99):1-5, 2012. [Abstract] [bibtex-entry]



9. E.J. Candes and Y. Plan. A Probabilistic and RIPless Theory of Compressed Sensing. IEEE Transactions on Information Theory, 57(11):7235-7254, November 2011. Keyword(s): Fourier coefficients, Gaussian model, RIPless theory, compressed sensing, frequency measurements, probabilistic theory, probability distribution, restricted isometry property, signal random model, sparse signals, Fourier analysis, data compression, random processes, signal reconstruction, statistical distributions;. [Abstract] [bibtex-entry]



10. E.J. Candes and Y. Plan. Matrix Completion With Noise. Proceedings of the IEEE, 98(6):925-936, june 2010. Keyword(s): compressed sensing, convex optimization problem, data constraints, low rank matrices, matrix completion, nuclear norm minimization, data integrity, matrix algebra, minimisation, noise, signal sampling;. [Abstract] [bibtex-entry]



11. Joachim H.G. Ender. On compressive sensing applied to radar. Signal Processing, 90(5):1402 - 1414, 2010. Note: Special Section on Statistical Signal and Array Processing. Keyword(s): Compressive Sensing, Compressed Sensing, Radar, Sparse arrays, Pulse compression, Radar imaging, ISAR, Airspace surveillance, DOA estimation. [Abstract] [bibtex-entry]



12. A. Zymnis, S. Boyd, and E. Candes. Compressed Sensing With Quantized Measurements. IEEE Signal Processing Letters, 17(2):149-152, February 2010. Keyword(s): Gaussian noise, compressed sensing, convex function, first order method, numerical simulation, quantized measurement, sparse signal estimation, Gaussian noise, quantisation (signal), signal processing;. [Abstract] [bibtex-entry]



13. R.G. Baraniuk, E. Candes, R. Nowak, and M. Vetterli. Compressive Sampling [From the Guest Editors]. IEEE Signal Processing Magazine, 25(2):12 -13, March 2008. [Abstract] [bibtex-entry]



14. Emmanuel J. Candes. The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique, 346(9-10):589-592, 2008. Keyword(s): Compressive Sensing, Compressed Sensing. [bibtex-entry]



15. E.J. Candes and M.B. Wakin. An Introduction To Compressive Sampling. IEEE Signal Processing Magazine, 25(2):21-30, March 2008. Keyword(s): Relatively few wavelet, compressed sensing, compressive sampling, data acquisition, image recovery, sampling paradigm, sensing paradigm, signal recovery, data acquisition, image processing, signal processing equipment, signal sampling;. [Abstract] [bibtex-entry]



16. Emmanuel J. Candes, Justin K. Romberg, and Terence Tao. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8):1207-1223, 2006. Keyword(s): Compressive Sensing, Compressed Sensing. [Abstract] [bibtex-entry]

Conference articles

1. O. Ponce, P. Prats, R. Scheiber, A. Reigber, and A. Moreira. First demonstration of 3-D holographic tomography with fully polarimetric multi-circular SAR at L-band. In Proc. IEEE Int. Geoscience and Remote Sensing Symp. - IGARSS, pages 1127-1130, July 2013. Keyword(s): airborne radar, array signal processing, compressed sensing, geophysical image processing, holography, optical focusing, optical tomography, radar imaging, radar polarimetry, radar resolution, synthetic aperture radar, transient response, 3D holographic tomography, 3D polarimetric holographic tomogram, 3D sidelobe reduction, BF, CS, DLR F-SAR airborne system, Earth analysis, IRF, Kaufbeuren Germany, L-band, MCSAR, beamforming, coherent 3D radar backscattering, compressive sensing, dry soil, forested area, fully polarimetric multicircular SAR, ice, impulse response function, multiangular measurement acquisition, temporal decorrelation, volume scatterer, Apertures, Bandwidth, Image resolution, Imaging, Synthetic aperture radar, Vegetation, Circular synthetic aperture radar (CSAR), compressive sensing (CS), fast factorized back-projection (FFBP), holographic tomography, polarimetric synthetic aperture radar (PolSAR). [bibtex-entry]



2. Esteban Aguilera, Matteo Nannini, and Andreas Reigber. Multi-signal compressed sensing for polarimetric SAR tomography. In Proc. IEEE Int. Geosci. Remote Sens. Symp., pages 1369-1372, July 2011. Keyword(s): Airborne SAR, SAR Processing, SAR Tomography, Tomography, Compressive Sensing, CS, Distributed Compressive Sensing, DCS methodology, E-SAR sensor, SAR image, azimuth-range pixel, distributed compressed sensing, elevation direction, fully polarimetric L-band data, multiplicative speckle noise, multisignal compressed sensing, pass acquisition geometry, polarimetric SAR tomography, polarimetric channel, space-borne radar platform, synthetic aperture radar, three dimensional imaging, tomographic focusing approach, tomographic signal, vertical reflectivity function, image reconstruction, image resolution, image sensors, radar imaging, radar polarimetry, radar resolution, spaceborne radar, speckle, synthetic aperture radar, tomography;. [Abstract] [bibtex-entry]

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This collection of SAR literature is far from being complete.
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