Results 41 to 50 of about 15,887 (242)
Leida xuebao, 2016 Sparse microwave imaging is new concept, theory and methodology of microwave imaging, which introduces the sparse signal processing theory to microwave imaging and combines them together to overcome the paradox of increasing system complexity and imaging
Zhang Zhe +3 more
doaj +1 more source
Sparse SAR Imaging and Quantitative Evaluation Based on Nonconvex and TV Regularization
Remote Sensing, 2021 Sparse signal processing has been used in synthetic aperture radar (SAR) imaging due to the maturity of compressed sensing theory. As a typical sparse reconstruction method, L1 regularization generally causes bias effects as well as ignoring region-based
Zhongqiu Xu +4 more
doaj +1 more source
A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR [PDF]
, 2018 $L_1$ regularization is used for finding sparse solutions to an underdetermined linear system. As sparse signals are widely expected in remote sensing, this type of regularization scheme and its extensions have been widely employed in many remote sensing
Bamler, Richard +3 more
core +3 more sources
SAR image reconstruction by expectation maximization based matching pursuit [PDF]
, 2015 Cataloged from PDF version of article.Synthetic Aperture Radar (SAR) provides high resolution images of terrain and target reflectivity. SAR systems are indispensable in many remote sensing applications.
Arikan, O., Gurbuz, A. C., Ugur, S.
core +1 more source
Joint space aspect reconstruction of wide-angle SAR exploiting sparsity [PDF]
, 2008 In this paper we present an algorithm for wide-angle synthetic aperture radar (SAR) image formation. Reconstruction of wide-angle SAR holds a promise of higher resolution and better information about a scene, but it also poses a number of challenges when
Cetin, Mujdat +3 more
core +1 more source
A nonquadratic regularization-based technique for joint SAR imaging and model error correction [PDF]
, 2008 Regularization based image reconstruction algorithms have successfully been applied to the synthetic aperture radar (SAR) imaging problem. Such algorithms assume that the mathematical model of the imaging system is perfectly known.
Cetin, Mujdat +3 more
core +1 more source
Deep learning in remote sensing: a review [PDF]
, 2017 Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields ...
Fraundorfer, Friedrich +6 more
core +4 more sources
Sparse PDE for SAR image speckle suppression
IET Image Processing, 2017 Speckle suppression is extremely important for understanding and utilising synthetic aperture radar (SAR) images, while the emphasis of the traditional methods for speckle suppression is usually focused on removing the noise instead of keeping the scattering character of imaging objects, which has caused serious interference to the subsequent ...
Zelong Wang +3 more
openaire +1 more source
Fast Compressed Sensing SAR Imaging based on Approximated Observation
, 2013 In recent years, compressed sensing (CS) has been applied in the field of synthetic aperture radar (SAR) imaging and shows great potential. The existing models are, however, based on application of the sensing matrix acquired by the exact observation ...
Fang, Jian +4 more
core +1 more source
Elinvar Materials: Recent Progress and Challenges
Advanced Engineering Materials, EarlyView.Elinvar materials, exhibiting temperature‐invariant elastic modulus, are critical for precision instruments and emerging technologies. This article reviews recent progress in the field, with a focus on the anomalous thermoelastic behavior observed in key material systems.
Wenjie Li, Yang Ren
wiley +1 more source