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Nonconvex Regularization in Remote Sensing
2016
IEEE Transactions on Geoscience and Remote Sensing
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We consider regularization via traditional squared (2) and sparsity-promoting (1) norms, as well as more unconventional nonconvex regularizers (p and Log Sum Penalty). ...
In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. ...
CONCLUSION In this paper, we have presented a general framework for nonconvex regularization in remote sensing image processing. ...
doi:10.1109/tgrs.2016.2585201
fatcat:zrokmneykfgdhdoe3hld2rn52q
An Accurate Sparse SAR Imaging Method for Enhancing Region-based Features via Nonconvex & TV Regularization
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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In order to reduce the bias effect and improve the reconstruction accuracy, we adopted the nonconvex regularization-based sparse SAR imaging method with a nonconvex penalty family. ...
With the rapid development of compressed sensing theories and applications, sparse signal processing has been widely used in synthetic aperture radar (SAR) imaging during the recent years. ...
INTRODUCTION S YNTHETIC aperture radar (SAR) is an active remote sensing technology, which carries on a moving platform, Manuscript transmits electromagnetic wave to the scene, receives radar echo, ...
doi:10.1109/jstars.2020.3034431
fatcat:2kivpkrxpvf4nisbh5ikweqvx4
Reference Information Based Remote Sensing Image Reconstruction with Generalized Nonconvex Low-Rank Approximation
2016
Remote Sensing
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remote sensing applications. ...
Because of the contradiction between the spatial and temporal resolution of remote sensing images (RSI) and quality loss in the process of acquisition, it is of great significance to reconstruct RSI in ...
Since the image prior knowledge plays a critical role in the performance of image reconstruction algorithms, designing effective regularization terms to reflect the image priors is the core of remote sensing ...
doi:10.3390/rs8060499
fatcat:6cia742r5ngdpcch2ru2nkltbe
Restoration of remote sensing images based on nonconvex constrained high-order total variation regularization
2019
Journal of Applied Remote Sensing
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Restoration of remote sensing images based on nonconvex constrained high-order total variation regularization," Abstract. ...
We consider a nonconvex second-order TV regularization model with linear constraints for remote sensing image restoration. ...
Introduction Image restoration has been widely studied in remote sensing image processing in the last decades. ...
doi:10.1117/1.jrs.13.022006
fatcat:iy6uq5kvondpvmwqq5xdwh5tme
Table of contents
2021
IEEE Transactions on Geoscience and Remote Sensing
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Yan 1534 Arbitrary Direction Ship Detection in Remote-Sensing Images Based on Multitask Learning and Multiregion Feature Fusion ...................................................................... ...
Zhang 1565 Robust Feature Matching for Remote Sensing Image Registration via Linear Adaptive Filtering ........................ ................................................................. X. ...
doi:10.1109/tgrs.2020.3048544
fatcat:qwhl3gqui5aezfcdyl7mcmadgm
Nonconvex L_ 1/2-Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction
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2022
arXiv
pre-print
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Recently, the nonconvex L_ 1/2-norm has achieved promising performance in sparse recovery, while the applications on imaging are unsatisfactory due to its nonconvexity. ...
In this paper, we develop a L_ 1/2-regularized nonlocal self-similarity (NSS) denoiser for CT reconstruction problem, which integrates low-rank approximation with group sparse coding (GSC) framework. ...
This work was supported in part by the Educational Commission of Hunan Province of China under grant No: 21B0466. ...
arXiv:2205.07185v1
fatcat:2zqy4mxho5cslnedlwtwjmf65i
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization
2016
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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In this paper, we propose a novel approach to hyperspectral image super-resolution by modeling the global spatial-andspectral correlation and local smoothness properties over hyperspectral images. ...
Specifically, we utilize the tensor nuclear norm and tensor folded-concave penalty functions to describe the global spatial-and-spectral correlation hidden in hyperspectral images, and 3D total variation ...
However, such HR images are not always easy to get due to the limitations of remote sensing system. ...
doi:10.1109/igarss.2016.7730816
dblp:conf/igarss/HeZWCH16
fatcat:qlfaapfjorhqtbtrbu5ag6uwoa
RFI Suppression for SAR via a Dictionary-Based Nonconvex Low-Rank Minimization Framework and Its Adaptive Implementation
2022
Remote Sensing
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regularizer and the low-rank regularizer. ...
In this paper, a novel dictionary-based nonconvex low-rank minimization (DNLRM) optimization framework is proposed for RFI suppression, which concurrently considers the improvements for both the sparse ...
Methods In this section, the nonconvex regularizer and the corresponding supergradient are firstly analyzed in a comparative manner. ...
doi:10.3390/rs14030678
fatcat:gkeagf7zgfgvjdmwqm2fq2ebg4
FasTer: Fast Tensor Completion with Nonconvex Regularization
[article]
2019
arXiv
pre-print
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In this paper, we propose to use the nonconvex regularizer, which can less penalize large singular values, instead of the convex one for tensor completion. ...
However, as the new regularizer is nonconvex and overlapped with each other, existing algorithms are either too slow or suffer from the huge memory cost. ...
Real-World Data Sets In this section, we perform evaluation on color images, remote sensing data, and social network data. ...
arXiv:1807.08725v3
fatcat:p55nji3pmze2xcqnjknm6wgx5u
An Overview on Linear Unmixing of Hyperspectral Data
2020
Mathematical Problems in Engineering
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A single pixel that leads to a hyperspectral remote sensing image usually contains more than one feature coverage type, resulting in a mixed pixel. ...
Hyperspectral remote sensing technology has a strong capability for ground object detection due to the low spatial resolution of hyperspectral imaging spectrometers. ...
to the quantitative development of remote sensing technology but also seriously affects the application of computer technology in remote sensing field. erefore, it is a key preprocessing to identify the ...
doi:10.1155/2020/3735403
fatcat:ijkjzzp6lbavhfhkyx7rwnxngy
Introducing the June Issue [From the Editor]
2021
IEEE Geoscience and Remote Sensing Magazine
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Contributions to our regular columns-"Chapters," "Technical Committees," "Space Agencies," "Women in Geoscience and Remote Sensing," "Education," "Software and Data Sets," and "Conference Reports"-are ...
This is a special issue on artificial intelligence (AI) innovation in geoscience and remote sensing. ...
doi:10.1109/mgrs.2021.3078586
fatcat:tluo6tchrfhdfkq26c4favcgiu
Table of contents
2021
IEEE Transactions on Geoscience and Remote Sensing
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Xie 5879 SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images .......................... D. Peng, L. Bruzzone, Y. Zhang, H. Guan, H. ...
Bao 5812 On Solving SAR Imaging Inverse Problems Using Nonconvex Regularization With a Cauchy-Based Penalty ......... ................................................................................... ...
doi:10.1109/tgrs.2021.3083444
fatcat:vlhsfoh76zf6zp6faqcfxuftfe
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations
2019
International Conference on Machine Learning
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Nonconvex regularizers have been successfully used in low-rank matrix learning. In this paper, we extend this to the more challenging problem of low-rank tensor completion. ...
Experimental results on a number of synthetic and realworld data sets show that the proposed algorithm is more efficient in both time and space, and is also more accurate than existing approaches. ...
sensing data. ...
dblp:conf/icml/YaoK019
fatcat:vuyefvzyqjf6xdf4bkbbrt2ela
Page 1924 of Mathematical Reviews Vol. , Issue 2002C
[page]
2002
Mathematical Reviews
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Russian summary) Avtomat. i Telemekh. 2000, no. 3, 66-75; translation in Autom Remote Control 61 (2000), no. 3, part 1, 416—424. ...
The solutions to such a problem have all the same tangential divergence (intended in a suitable weak sense related to the geometry induced by ®), and such tangential divergence is by definition the ®-mean ...
ENDMEMBER EXTRACTION OF HIGHLY MIXED DATA USING L1 SPARSITY-CONSTRAINED MULTILAYER NONNEGATIVE MATRIX FACTORIZATION
2018
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Due to the limited spatial resolution of remote hyperspectral sensors, pixels are usually highly mixed in the hyperspectral images. ...
Besides, to improve the performance of NMF, we incorporated sparsity constraints to the multilayer NMF model by adding a <i>L</i><sub>1</sub> regularizer of the abundance matrix to each layer. ...
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3, 2018 ISPRS TC III Mid-term Symposium "Developments, Technologies and Applications in Remote ...
doi:10.5194/isprs-archives-xlii-3-329-2018
fatcat:vp5ool7vfnbyfchds6j5twkvdy
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