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Nonconvex Regularization in Remote Sensing

Devis Tuia, Remi Flamary, Michel Barlaud
2016 IEEE Transactions on Geoscience and Remote Sensing  
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

Zhongqiu Xu, Mingqian Liu, Guoru Zhou, Zhonghao Wei, Bingchen Zhang, Yirong Wu
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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

Hongyang Lu, Jingbo Wei, Lizhe Wang, Peng Liu, Qiegen Liu, Yuhao Wang, Xiaohua Deng
2016 Remote Sensing  
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

Jianguang Zhu, Kai Li, Binbin Hao
2019 Journal of Applied Remote Sensing  
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  
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 [article]

Yunyi Li
2022 arXiv   pre-print
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

Shiying He, Haiwei Zhou, Yao Wang, Wenfei Cao, Zhi Han
2016 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
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

Zhouyang Tang, Yunkai Deng, Huifang Zheng
2022 Remote Sensing  
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]

Quanming Yao, James T Kwok, Bo Han
2019 arXiv   pre-print
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

Jiaojiao Wei, Xiaofei Wang
2020 Mathematical Problems in Engineering  
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]

James L. Garrison
2021 IEEE Geoscience and Remote Sensing Magazine  
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  
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

Quanming Yao, James Tin-Yau Kwok, Bo Han
2019 International Conference on Machine Learning  
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  
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

H. Fang, A. H. Li, H. X. Xu, T. Wang, K. Jiang, Y. Z. Su, H. W. Chang
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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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