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HIGH-ORDER PROXIMAL POINT ALGORITHM FOR THE MONOTONE VARIATIONAL INEQUALITY PROBLEM AND ITS application

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arXiv 2023年

作者： Gao, Jingyu Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences China

The proximal point algorithm (PPA) has been developed to solve the monotone variational inequality problem. It provides a theoretical foundation for some methods, such as the augmented Lagrangian method (ALM) and the alternating direction method of multipliers (ADMM). This paper generalizes the PPA to the pth-order (p ≥ 1) and proves its convergence rate O(1/kp/2). Additionally, the pth-order ALM is proposed based on the pth-order PPA. Some numerical experiments are presented to demonstrate the performance of the pth-order ALM. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization

A LINEARLY CONVERGENT METHOD FOR SOLVING HIGH-ORDER PROXIMAL OPERATOR∗

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arXiv 2023年

作者： Jingyu, G.A.O. Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences China

Recently, various high-order methods have been developed to solve the convex optimization problem. The auxiliary problem of these methods shares the general form that is the same as the high-order proximal operator proposed by Nesterov. In this paper, we present a linearly convergent method to solve the high-order proximal operator based on the classical proximal operator. In addition, some experiments are performed to demonstrate the performance of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization

DENSE-ADD NET: AN NOVEL CONVOLUTIONAL NEURAL NETWORK FOR REMOTE SENSING IMAGE INPAINTING

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DENSE-ADD NET: AN NOVEL CONVOLUTIONAL NEURAL NETWORK FOR REM...

IEEE International Geoscience and Remote Sensing Symposium

作者： Daoyu Lin Guangluan Xu Yang Wang Xian Sun Kun Fu School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Through the recent performance of convolutional neural networks in image processing tasks, we propose a deep fully convolutional network for remote sensing image inpainting. The proposed Dense-Add Net (Dense-Add Network) can alleviate the vanishing-gradient problem, strengthen feature reuse, and substantially reduce the memory usage. We apply residual learning to learn the mappings from corrupted image to recovered image directly;it will back-propagate gradient to the bottom layers and accelerate the training process. We train the proposed Dense-Add Net with a robust Charbonnier loss function which can achieve high-quality reconstruction. The experimental verify the efficacy of our proposed Dense-Add Net.

关键词： Remote sensing Training Acceleration Image reconstruction Painting Convolution Interpolation

In-Plane Optical Anisotropy of Two-Dimensional ReS2 and ReSe2 Based on Imaging Spectroscopic Ellipsometry

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SSRN

SSRN 2024年

作者： Wang, Yueming Wei, Mingyang Han, Yating Lian, Jie School of Information Science and Engineering Shandong Provincial Key Laboratory of Laser Technology and Application Shandong University Qingdao266237 China Center for Optics Research and Engineering Key Laboratory of Laser & Infrared System Ministry of Education Shandong University Qingdao266237 China

The presence of the in-plane "rhenium chain" contributes to the anisotropy of two-dimensional (2D) ReX2 (X=S or Se), which makes them more attractive in the 2D transition metal dichalcogenides (TMDs). Extensive research on the optical anisotropy of the 2D ReX2 has provided essential guidance for their fabrication and optimization in optoelectronic devices. The periodic variation of the two Eg phonon modes with the direction of laser polarization has been detected using polarized Raman spectroscopy, confirming the structural anisotropy of the 2D ReX2. In contrast to the conventional ellipsometer employing a spot-averaging technique, this study introduces an imaging spectroscopic ellipsometer (ISE) capable of achieving lateral resolutions up to approximately 2 μm. The refractive index, extinction coefficient, and dielectric function spectra along the a-axis and b-axis of the 2D ReX2 with micron-scale were measured by rotating the sample azimuthal angle in the visible light range (400-700 nm), filling the gap in partial optical parameter database of TMDs. Furthermore, the birefringence and dichroism of the 2D ReX2 are analyzed to serve as a reference for its application in optical devices, such as phase-modulation devices near 700 nm. In addition, the ISE with high lateral resolution, which can avoid the bulky data processing of the Mueller matrix, reported in this paper, offers a novel approach for quantifying the intrinsic optical anisotropy of 2D materials. © 2024, The Authors. All rights reserved.

关键词： Optical anisotropy

Spatial-temporal water area monitoring of Miyun Reservoir using remote sensing imagery from 1984 to 2020

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arXiv 2021年

作者： Liu, Chang Tang, Hairong Ji, Luyan Zhao, Yongchao Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

Miyun Reservoir has produced huge benefits in flood control, agricultural irrigation, power generation, aquaculture, tourism, and urban water supply. Accurately water mapping is of great significance to the ecological environment monitoring of the Miyun Reservoir and the management of the South-to-North Water Diversion Project. On the 60th anniversary of the completion of the Miyun Reservoir, we took the Miyun Reservoir as the study area and collected all the Landsat-5 and Landsat-8 remote sensing images from 1984 to 2020 for water mapping. Based on the spectral, topographical and temporal-spatial characteristics of water, we proposed an automated method for long-term researvoir mapping, which can solve the problems caused by cloud, shadow, ice and snow pixels. Moreover, it can also deal with the "same objects with different spectra" and spectral mixed problems. The overall accuracy is as high as 98.2% for the case with no cloud or snow/ice cover. The landscape division index is introduced to analyze the morphological changes of Miyun Reservoir. Based on the mapping results, it is suggested that the water area change of the reservoir can be divided into five periods: "growth period" (1984-1993), "peak period" (1994-1999), "decline period" (2000-2003), "protection period" "(2004-2014), and "recovery period" (2015-2020), during which the east and west areas of the reservoir have experienced the process of "division-combination-division-combination". The largest reservoir area reached 151.6 km2 (1995), and the smallest area was only 57.3 km2 (2004). Moreover, we also find that the largest area in a year occurs from August to September, and the smallest area occurs in May. In addition, we have found that the changes of water area of Miyun Reservoir are closely connected to urban development, government policies and precipitation varitations. The study shows that our method used here are potentially extendable to other reservoirs for long-term area monitoring. © 2021, CC

关键词： Time series analysis

Continual Semantic Segmentation via Mask-Based Class Rebalancing

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Continual Semantic Segmentation via Mask-Based Class Rebalan...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yongjie Guo Siya Chen Hongjian You Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Continual semantic segmentation (CSS) has risen as a popular field, which aims to acquire new skills constantly without forgetting past knowledge catastrophically. In CSS, we identify that there is a severe imbalance between new classes and old classes, leading to the classifier weight toward new classes. In this paper, we deal with the continual semantic segmentation problem from the class imbalance perspective via mask-based class rebalancing, avoiding the model suffering from catastrophic forgetting. More specifically, the mask-based class rebalancing depends on a mask to combine resampling with reweighting ingenuously, which mitigates the classifier bias toward new classes. Besides, we also propose a frequency knowledge distillation, leveraging multiple frequency components information to maintain the feature representation space for old classes. We demonstrate the effectiveness of our approach with an extensive evaluation of the Pascal-VOC 2012 and ADE20K datasets, significantly outperforming the state-of-the-art method.

关键词： Semantic segmentation Semantics

Dissipative Soliton Resonance and Noise-Like Pulse in a Self-Pulsing Fiber Laser

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SSRN

SSRN 2022年

作者： Ma, Xiaoran Feng, Tianli Zhao, Shengzhi Liu, Yizhou Wang, Chun Shang, Jingcheng Hu, Ping Mao, Jiajia Yang, Kejian Li, Tao China Key Laboratory of Laser & Infrared System Ministry of Education Shandong Provincial Key Laboratory of Laser Technology and Application School of Information Science and Engineering Shandong University Qingdao266237 China Institute of Novel Semiconductors State Key Laboratory of Crystal Materials Shandong University Jinan250100 China

In this paper, a self-pulsing Er-doped fiber laser operating in the dissipative soliton resonance (DSR) regime is firstly reported. By adjusting the pumping power and intracavity pulse polarization, the generated mode-locking pulse could evolve between soliton pulse, dissipative soliton resonance pulse and noise-like pulse (NLP). In the dissipative soliton resonance mode-locking regime, the generated rectangular-shaped pulses centered at 1570.5 nm with a maximum average output power of 20.6 mW and a pulse duration of 8.9 ns. The self-pulsing evolving dynamics can be analyzed using weak NPR effect by simultaneously considering the polarization dependent loss from the polarization controller and the reabsorption loss of the un-pumped gain fiber. © 2022, The Authors. All rights reserved.

关键词： Fiber lasers

Airborne Polarimetric and Repeat-Pass SAR Interferometry Campaign: L-Band Results

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Airborne Polarimetric and Repeat-Pass SAR Interferometry Cam...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xuelian Zhong Li Tao Jiajia Zhang Xi Chen Mingxing Shen Rengli Liu Hong Hu Jianli Liu Key Laboratory of Aperture Array and Space Application No.38 Research Institute China Electronics Technology Group Corporation HeFei China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Beijing China

From September to November, 2018, a L-band SAR system developed by the No.38 Research Institute, China Electronics technology Group Corporation (CETC 38) took several flights for testing and validation. Polarimetric and repeat-pass interferometric SAR data were acquired in these flights. This paper mainly presents the polarimetric SAR images and repeat-pass interferometric SAR (InSAR) results in this campaign.

关键词： Synthetic aperture radar L-band Interferometry Azimuth Radar polarimetry Focusing Estimation

Rethinking the key Factors for the Generalization of Remote Sensing Stereo Matching Networks

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arXiv 2024年

作者： Jiang, Liting Wang, Feng Zhang, Wenyi Li, Peifeng You, Hongjian Xiang, Yuming Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Stereo matching, a critical step of 3D reconstruction, has fully shifted towards deep learning due to its strong feature representation of remote sensing images. However, ground truth for stereo matching task relies on expensive airborne LiDAR data, thus making it difficult to obtain enough samples for supervised learning. To improve the generalization ability of stereo matching networks on cross-domain data from different sensors and scenarios, in this paper, we dedicate to study key training factors from three perspectives. (1) For the selection of training dataset, it is important to select data with similar regional target distribution as the test set instead of utilizing data from the same sensor. (2) For model structure, cascaded structure that flexibly adapts to different sizes of features is preferred. (3) For training manner, unsupervised methods generalize better than supervised methods, and we design an unsupervised early-stop strategy to help retain the best model with pre-trained weights as the basis. Extensive experiments are conducted to support the previous findings, on the basis of which we present an unsupervised stereo matching network with good generalization performance. We release the source code and the datasets at https://***/Elenairene/RKF_RSSM to reproduce the results and encourage future work. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning